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This paper takes Shengwan-Shigu area, which is located at the junction of Southwest Henan Province and Hubei Province, as the research area. Eleven evaluation factors are selected, and the multiple regression analysis function of SPSS software is used to analyze the sensitivity of each index. The sensitivity analysis results of the evaluation index show that the geological environment quality in the study area is the most sensitive to the changes of soil and water loss and desertification. Then establish the geological environment quality evaluation index system. In this paper, the Tensorflow deep learning library based on Python language is used to construct the neural network model to evaluate the geological environment quality of the study area; by using Tensorboard visualization tool, the complex neural network training process is visualized, and the neural network model is debugged and optimized. Finally, the evaluation results are visualized by the map editing function of MAPGIS software. In this paper, the geological environment quality is divided into three levels: good area, middle area and poor area. The evaluation results show that the overall conditions of the study area are good. The results of geological environment zoning and geological hazard survey points superposition in the study area show that the evaluation process and results are reasonable and feasible. The research conclusion of this paper can provide scientific basis for regional geological environment management in areas with serious soil and water loss and rocky desertification, and has important practical significance.
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