Abstract: To gain environmental comprehensive evaluation objectively is one major subject of mine environment research，which contributes to sustainable development and utilization of local resources and the ecological environment restoration. There are many kinds of mine environmental impact factors and quantitative evaluation process is vulnerable to human factors intervention. BP neural network and SVM algorithm have the information processing ability and reasoning function to simulate the nonlinear relationship between each factor automatically. Based on remote sensing survey results of ore concentration in Qinghe Country，Xinjiang，China，the BP and SVM evaluation models，introduced to the mine environment evaluation for the first time，with 14 variables as input vector and unite score as output vector，have obtained good results. Both models select 160 units as the training sample that contains 4 large properties: natural geographical，basic geology，development covering and geological environment. The results show that both models can meet accuracy requirements of the mine environmental assessment （most of absolute error magnitude of the validation data is less than the grade e-1，77.5% and 95%，respectively）；MSE of SVM model （7.39 e-4） is smaller than that of BP neural network （1.5 e-3）；SVM model，whose convergence rate is faster than BP neural network，is more suitable for mine environmental evaluation. Environmental score is divided into 4 levels that is consistent with qualitative evaluation，so it indicates the model is worthy of promotion.

Key words: mine environmental evaluation, BP neural network, Support Vector Machine, GIS