## 1 Introduction
The advantage of the entropy method is that it can objectively give the weight of each factor, but the disadvantage is that the weight of each classification within a single factor needs to be \"subjectively given\"; however, the information quantity method is the opposite, and is advantage is that it can well calculate the internal value of each classification within a single factor (the amount of information), but the disadvantage is that the weight of each factor needs to be \"subjectively given\". The rapid development of \"3S\" technology has effectively improved the evaluation accuracy of the regional geological disaster sensitivity evaluation model, especially the combination of different modeling methods based on GIS has been widely used in geological disaster sensitivity evaluation and achieved good results. And most scholars believe that the evaluation effect of multiple methods is better than that of a single method. [PERSON] et al. (2012) used the geostatistical informativeness method in Wufeng County, Hubei Province as an example, selected and analyzed each disaster-causing factor from the disaster-causing mechanism based on GIS technology and RS technology, and completed the quantitative evaluation of geologic disaster susceptibility zoning using GIS spatial analysis technology [33]. [PERSON] et al. (2018) evaluated the susceptibility of Yanchi County in Ningxia through the quantitative information quantity method and the coefficient of determination method coupled with logistic regression, respectively, and concluded that the results of the coupled models were both better than the results of the single model evaluation [34]. [PERSON] et al. (2021) used the informativeness model and logistic regression model to establish the informativeness model and the informativeness method and logistic regression model for the evaluation of geological disaster susceptibility in the Utopia District of Shiyan City, Hubei Province, as an example [35], [PERSON] et al. (2021) talked about risk assessment and its influencing factors in the analysis of geological hazards in typical mountain environments [36], and [PERSON] et al. (2023), [PERSON] et al. (2023) and [PERSON] et al. (2023) used the informativeness method to carry out a comparative study of geological hazard risk assessment pairs and concluded that the validity of the method was better [37; 38; 39]. [PERSON] et al. (2021) applied machine learning and statistical methods to assess the susceptibility of torrential-rainfall-induced landslides in the eastern Himalayas [40]; they compared and analyzed the results of the evaluation, and through the test of significance, it was concluded that the method was able to objectively respond to the susceptibility to geological disasters with better results. [PERSON] et al. (2019) used the integrated analytic hierarchy process and GIS tech nique to analyze potential landslide vulnerability zonation of the Upper Rangit Catchment Area, West Sikkim, India [41]. Deep learning evolved from simple perceptron artificial neural networks, but perceptron machines have difficulty solving more complex nonlinear problems. Although the BP (backpropagation) neural network is generated through the error backpropagation algorithm of a multilayer perceptron with a nonlinear continuous transformation function, the BP neural network model uses the backpropagation method to update the weights of neurons according to the error rate, and adjusting the weight values based on the local gradient descent is prone to the gradient diffusion phenomenon, and this phenomenon increases with the increase in network layers. This problem restricts the development of neural networks. Deep learning algorithms support their learners by extracting high-complexity data table evidence. This data extraction mechanism provides raw data for machine learning and enables these \"learners\" to automatically discover appropriate data representations, while deep learning algorithms produce better results in different learning applications.