Traditional methods for assessing tourist ecological capacity in forest parks often use single indicators or static models, failing to capture dynamic ecological changes. This study introduces a novel approach leveraging fusion Attention Mechanisms (AM) and Remote Sensing (RS) images to evaluate the ecological capacity of visitors in forest parks. The method uses high-resolution RS images and deep learning to accurately measure the impact of tourist activities on the ecological environment. The proposed method includes an image reconstruction technique that integrates AM with RS data. Performance analysis and validation are conducted, showing an average accuracy of 98.76%, a recall rate of 98.17%, and an F1 score ranging from 96.23% to 98.25% in the Fujian Regional Remote Sensing Image Dataset for Scene classification (FJ-RSIDS). Applying this method to Qilian Mountain Park, the study predicts ecological capacities of 31.43 million for the environment, 104.83 million for space, 115.74 million for tourist psychology, 334,200 for facilities, and 64.84 million for tourism. These predictions closely align with observed values. This research provides a scientific basis and technical support for ecological protection and tourist management in forest parks, contributing significantly to the sustainable development of the tourism industry.

[1] Ai B., Xiao H., Xu H., Yuan F., and Ling M., “Coastal Aquaculture Area Extraction Based on Self-Attention Mechanism and Auxiliary Loss,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, no. 1, pp. 2250-2261, 2023. DOI: 10.1109/JSTARS.2022.3230081

[2] Arpitha M., Ahmed S., and Harishnaika N., “Land Use and Land Cover Classification Using Machine Learning Algorithms in Google Earth Engine,” Earth Science Informatics, vol. 16, no. 4, pp. 3057-3073, 2023. https://link.springer.com/article/10.1007/s12145- 023-01073-w

[3] Brauwers G. and Frasincar F., “A General Survey on Attention Mechanisms in Deep Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3279-3298, 2023. DOI: 10.1109/TKDE.2021.3126456

[4] Chen B., Liu T., He C., Liu Z., and Zhang L., “Fault Diagnosis for Limited Annotation Signals and Strong Noise Based on Interpretable Attention Mechanism,” IEEE Sensors Journal, vol. 22, no. 12, pp. 11865-11880, 2022. DOI: 10.1109/JSEN.2022.3169341

[5] Chen W., Ouyang S., Tong W., Li X., Zheng X., and Wang L., “GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, no. 1, pp. 1150-1162, 2022. DOI: 10.1109/JSTARS.2022.3141826

[6] Chen Z., Han X., and Ma X., “Combining Contextual Information by Integrated Attention Mechanism in Convolutional Neural Networks for Digital Elevation Model Super-Resolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, no. 8, pp. 1-16, 2024. DOI: 10.1109/TGRS.2024.3423716

[7] Chi K., Yuan Y., and Wang Q., “Trinity-Net: Gradient-Guided Swin Transformer-based Remote Sensing Image Dehazing and Beyond,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, no. 12, pp. 1-14, 2023. DOI: 10.1109/TGRS.2023.3285228

[8] Guo R., Liu H., Xie G., Zhang Y., and Liu D., “A Self-Interpretable Soft Sensor Based on Deep Learning and Multiple Attention Mechanism: From Data Selection to Sensor Modeling,” IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 6859-6871, 2023. DOI: 10.1109/TII.2022.3181692

[9] Hao Y., Wang S., Gao P., Gao X., and Xu T., “Attention in Attention: Modeling Context Correlation for Efficient Video Classification,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 7120-7132, 2022. DOI: 10.1109/TCSVT.2022.3169842

[10] He X., Zhou Y., Zhao J., Zhang D., Yao R., and Xue Y., “Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 1, pp. 1-15, 2022. DOI: 10.1109/TGRS.2022.3144165

[11] Hussain M., Chen T., Titrenko S., Su P., and Mahmud M., “A Gradient Guided Architecture Coupled with Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces,” IEEE Access, vol. 10, no. 8, pp. 58950- 58964, 2022. DOI: 10.1109/ACCESS.2022.3178588

[12] Ji Z., Lv T., and Yu Y., “A Dual-End Recommendation Algorithm Integrating User Intent and Knowledge-Aware Attention Networks,” The International Arab Journal of Information Technology, vol. 22, no. 3, pp. 505- 521, 2025, DOI: 10.34028/iajit/22/3/7

[13] Khataei A., Singh G., and Bazargan K., “Optimizing Hybrid Binary-Unary Hardware Accelerators Using Self-Similarity Measures,” in Proceedings of the IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines, California, pp. 105-113, 2023. DOI: 10.1109/FCCM57271.2023.00020

[14] Khataei A., Singh G., and Bazargan K., “SimBU: Self-Similarity-Based Hybrid Binary-Unary Computing for Nonlinear Functions,” IEEE Transactions on Computers, vol. 73, no. 9, pp. 2192-2205, 2024. DOI: 10.1109/TC.2024.3398512

[15] Liang Z., Tao M., Xie J., Yang X., and Wang L., “A Radio Signal Recognition Approach Based on Complex-Valued CNN and Self-Attention Mechanism,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 3, pp. 1358-1373, 2022. DOI: 10.1109/TCCN.2022.3179450

[16] Liu C., Zhao R., Chen H., Zou Z., and Shi Z., “Remote Sensing Image Change Captioning with Dual-Branch Transformers: A New Method and a Large Scale Dataset,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 9, pp. 1-20, 2022. DOI: 10.1109/TGRS.2022.3218921

[17] Lu Y., Li J., Song Z., and Lu M., “Evaluating Sustainable Development in the Middle and Lower Reaches of the Yellow River Basin Using Multiple Data Sources,” IEEE Transactions on Geoscience and Remote Sensing, vol. 63, no. 2, pp. 1-18, 2025. DOI: Ecological Capacity Assessment of Forest Park Tourists by Integrating Attention ... 961 10.1109/TGRS.2024.3509934

[18] Luo Z., Xu A., and Gao S., “Research on the Coupling Coordination Relationship between Tourism Carrying Capacity and the High-Speed Railway Network: A Case Study in China,” Access, vol. 11, no. 6, pp. 20426-20444, 2023.

[19] Lv Z., Huang H., Li X., Zhao M., Benediktsson J., and Sun W., “Land Cover Change Detection with Heterogeneous Remote Sensing Images: Review, Progress, and Perspective,” Proceedings of the IEEE, vol. 110, no. 12, pp. 1976-1991, 2022. DOI: 10.1109/JPROC.2022.3219376

[20] Peng X., Shu W., Pan C., Ke Z., Zhu H., and Zhou X., “DSCSSA: A Classification Framework for Spatiotemporal Features Extraction of Arrhythmia Based on the Seq2Seq Model with Attention Mechanism,” IEEE Transactions on Instrumentation and Measurement, vol. 71, no. 5, pp. 1-12, 2022. DOI: 10.1109/TIM.2022.3194906

[21] Preethi P. and Mamatha H., “Region-based Convolutional Neural Network for Segmenting Text in Epigraphical Images,” Artificial Intelligence and Applications, vol. 1, no. 2, pp. 119-127, 2023. https://doi.org/10.47852/bonviewAIA2202293

[22] Roy S., Deria A., Hong D., Rasti B., Plaza A., and Chanussot J., “Multimodal Fusion Transformer for Remote Sensing Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, no. 12, pp. 1-20, 2023. DOI: 10.1109/TGRS.2023.3286826

[23] Su X., Li J., and Hua Z., “Transformer-based Regression Network for Pansharpening Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 6, pp. 1-23, 2022. DOI: 10.1109/TGRS.2022.3152425

[24] Tang D., Cao X., Hou X., Jiang Z., Liu J., and Meng D., “CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, no. 3, pp. 1-14, 2024. DOI: 10.1109/TGRS.2024.3453414

[25] Tutek M. and Snajder J., “Toward Practical Usage of the Attention Mechanism as a Tool for Interpretability,” IEEE Access, vol. 10, no. 2, pp. 47011-47030, 2022. DOI: 10.1109/ACCESS.2022.3169772

[26] Wang W., Zhang L., Fu S., Ren P., Ren G., and Peng Q., “Gradient Guided Multiscale Feature Collaboration Networks for Few-Shot Class- Incremental Remote Sensing Scene Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, no. 5, pp. 1-12, 2024. DOI: 10.1109/TGRS.2024.3369083

[27] Xiao Y., Yuan Q., Jiang K., He J., Jin X., and Zhang L., “EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution,” IEEE Trans. Geosci. Remote Sens., vol. 62, no. 5, pp. 1-14, 2024. DOI: 10.1109/TGRS.2023.3341437

[28] Xiao Y., Yuan Q., Jiang K., He J., Lin C., and Zhang L., “TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super- Resolution,” IEEE Transactions on Image Processing, vol. 33, no. 7, pp. 738-752, 2024. DOI: 10.1109/TIP.2023.3349004

[29] Yan R., Han Y., Li F., and Li P., “Evaluation of Sustainable Potential Bearing Capacity of Tourism Environment Under Uncertainty: A Multiphase Intuitionistic Fuzzy EDAS Technique Based on Hamming Distance and Logarithmic Distance Measures,” Access, vol. 12, no. 9, pp. 8081-8095, 2024.

[30] Yang S., Tian Y., He C., Zhang X., Tan K., and Jin Y., “A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 4861-4875, 2022. DOI: 10.1109/TNNLS.2021.3061630

[31] Yu W., Huang H., and Shen G., “Deep Spectral- Spatial Feature Fusion-based Multiscale Adaptable Attention Network for Hyperspectral Feature Extraction,” IEEE Transactions on Instrumentation and Measurement, vol. 72, no. 1, pp. 1-13, 2023. DOI: 10.1109/TIM.2022.3222480

[32] Zhang S., Qi X., Duan J., Yuan X., Zhang H., and Feng W., “Comparison of Attention Mechanism- based Deep Learning and Transfer Strategies for Wheat Yield Estimation Using Multisource Temporal Drone Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, no. 4, pp. 1-23, 2024. DOI: 10.1109/TGRS.2024.3401474

[33] Zhao J., Yu C., Shi Z., Liu Y., and Zhang Y., “Gradient-Guided Learning Network for Infrared Small Target Detection,” IEEE Geoscience and Remote Sensing Letters., vol. 20, no. 6, pp. 1-5, 2023. DOI: 10.1109/LGRS.2023.3308783