Accurate land use/land cover data can not only reflect the regional ecological environment and provide decision support for environmental departments, but also play an important role in achieving higher quality development of regional ecological environment. Taking the middle and lower reaches of Ziwu River Basin as the study area, using multi-source and multi temporal Landsat 8 satellite remote sensing data, combined with ground survey data and literature survey, this study discusses and studies the support vector machine classification (SVM) and random forest model (RFM) to identify the vegetation types and land use status types in this area, and compares the classification accuracy of the two methods. The importance and applicability of spectral characteristic variables to the model are analyzed and evaluated. Based on the data of land use status, combined with the modified general soil loss equation RUSLE model, the soil erosion modulus of the study area was further calculated, the distribution map of soil erosion in the study area was drawn, and the ecological environment index of the study area was calculated according to the land use vegetation cover information, and the ecological environment evaluation of the tourism basin under meridian river insects was carried out from a macro point of view. To the extent that it is not possible to make a difference between the two groups. The results show that:① the random forest model can effectively use the characteristic factors of samples and combine them with topographic constraints to classify vegetation and land use types. The overall classification accuracy is more than 80%, and the kappa coefficients are 0.73 and 0.86 respectively. Compared with the traditional SVM method, RFM method improves the classification accuracy of forest types and land use types. ② The overall Eco-environmental Status Index of the study area is 87.12, and the eco-environmental status is excellent. Due to the relatively large amount of soil erosion near the water source area, the eco-environmental status is good, accounting for 15.69% of the total area of the study area.