The evolution of sparse vegetation cover (grassland, sandy land, Gobi) can directly characterize the dynamic changes of the regional ecological environment and human activities. However, large-scale sparse vegetation areas generally have large geographic spans, complex and diverse landscape structures, high fragmentation, and insufficient pertinence of existing land cover classification products. These problems make it difficult to extract the forest, grass, and sand in this area by remote sensing, and the accuracy is generally low, which directly restrict the application results of the ecological effect evaluation model. Therefore, this study took the typical large-scale sparse vegetation area, the Beijing-Tianjin sandstorm source region (BTSSR) as the study area, and the SNIC-CNN-SVM (SCS) model is proposed. The forest, grass, sand element information is automatically extracted and the land use/land cover types are identified in the study area. The results show that:1) the SNIC segmentation algorithm was optimized by the penalty mechanism and can effectively increase the boundary discrimination between the sparse vegetation area and the sandy area, which is helpful to improve the classification accuracy; 2) the overall classification accuracy of the optimized model reached 89.41%, which is 11.17% higher than that before optimization, especially the classification and recognition accuracy of trees, shrubs, grasses, sandy land and Gobi is significantly improved, indicating that the optimization model has good application effect and promotion value in the classification of sparse vegetation areas represented by the study area; 3) in 2020, the grassland area in the study area was the largest, accounting for more than half (51.52%), sandy land accounted for 11.96%, and sparse vegetation coverage (grassland, sandy land, Gobi) accounted for 68.68%, indicating that the project area is in the transition zone of woodland-sparse vegetation-sandy land, and the pressure on ecological environment protection and the situation of desertification prevention and control are still severe; 4) in the past 20 years, the increased area of vegetation such as arbor, shrub and grass accounted for about 20.64% of the project, which is mainly derived from desertified land. The reduced area of desertified land accounted for about 4.58% of the project area, indicating that the vegetation conditions in the study area are continuously improving, and the implemented ecological projects have a significant effect. The method in this paper can be more effectively used for multi-dimensional ecosystem service function evaluation. This study is expected to provide important scientific support for the research on the evolution law of the ecosystem and the evaluation of ecological engineering in the BTSSR.