## 6 Conclusions  
This paper presents a model called SFFNet that combines a CNN and GCN for HSI classification. To address the challenge of constructing the graph data structure of original HSIs, we first utilized the CNN framework to extract the original features. Then, we constructed the graph data structure. This structure was input into the GCN to complete the final classification. The framework leverages topological information to ensure that local image information complements global information, allowing for flexible feature capture and substantial improvement in classification performance across various datasets. Compared to the single model, SFFNet optimizes the shortcomings of CNNs by using local convolution and a GCN based on full-batch input. The model further provides deep extraction of spectral-spatial information, which makes the model more suitable for HSIs. Compared with the multi model, SFFNet optimizes the fusion method between models. This fusion method enables the original model to better reflect its characteristics. Based on the experimental results, SFFNet consistently achieved the best performance. In quantitative comparisons, SFFNet exhibited the highest classification accuracy. In qualitative comparisons, SFFNet demonstrated the best classification effect. It is evident that SFFNet excels in category differentiation, particularly in distinguishing edge categories. When handling boundary information, the CNN's fixed convolutional kernel indiscriminately extracts features. However, the introduction of the GCN makes the model more flexible in feature extraction and preferring features with higher similarity. Additionally, the incorporation of neighboring nodes in SFFNet enables the model to better differentiate the categories of surrounding nodes. For HSIs, spectral information is the most important feature. The spectral similarity structure in SFFNet makes the model more sensitive to differences between spectra. Consequently, SFFNet possesses a strong advantage in feature extraction and differentiation, thereby effectively completing classification tasks to a high standard. However, SFFNet also has its limitations. Although SFFNet can achieve better results with the same number of iterations, it requires higher time costs compared to other models. In the future work, we will further update the model so that the model continues to maintain its stability and high accuracy on the basis of reducing the time overhead. For example, by choosing a more lightweight CNN structure to reduce the parameters and computation, or by updating the sampling method of SAGE. Conceptualization, H.L. and X.X. ; methodology, X.X and C.L. ; software, Y.M. ; validation, H.L., Y.L. and S.Z. ; formal analysis, H.L., X.X. and C.L. ; resources, H.L. and C.L. ; data curation, X.X. ; writing--original draft preparation, H.L. and X.X. ; writing--review and editing, H.L., C.L. and Y.M. ; visualization, X.X. and S.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by Hubei's Key Project of Research and Development Program under Grant 2023 BB046, and National Natural Science Foundation of China under Grant U23B2050, and Excellent young and middle-aged scientific and technological innovation teams in colleges and universities of Hubei Province under Grant T2021009, and NSFC-CAAC under Grant U1833119, and Science and Technology Program of Hubei Provincial Education Department under Grant D20221604, and the Natural Science Foundation of Hubei Province of China under Grant 2023 AFB351, and the University-Industry Collaborative Education Program under Grant 230705841293521. Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study.