Mine ecological environment evaluation and rehabilitation based on migration learning image recognition algorithm and hierarchical analysis

Artificial intelligence and intelligent algorithms are gradually becoming the key technology to manage the ecological environment of mines, but the existing ecological evaluation and restoration methods have the shortcomings of large evaluation bias and slow restoration, so the study proposes a mine ecological environment evaluation and restoration model based on migration learning image recognition algorithm and hierarchical analysis. It was found that the error of the model in evaluating the ecological indicators was 2.24%, the average time for generating the restoration program was 7.5 d, the vegetation coverage was 23.4%, and the soil erosion modulus decreased to 61.8 t/(km²・a). The above experimental data are better than the comparison model, which fully proves the feasibility and superiority of the research model and contributes to the study of evaluation and restoration of mine ecological environment.