The invention discloses a mine
ecological safety identification method based on a
convolutional neural network model, and relates to the technical field of mine
ecological safety identification. A
convolutional neural network CNN is introduced to automatically optimize a resistance value, a time dimension and a dynamic factor are combined, and a resistance surface is monitored and adjusted in real time, so that the safety of mine
ecological safety identification is improved. The method can dynamically respond to the influence of topographic change,
vegetation density fluctuation and meteorological change, can capture the real-time influence of sudden rainfall, mining area extension or
vegetation restoration and the like, the generated resistance surface has high timeliness and accurately reflects the dynamic characteristics of an ecological
system, and meanwhile, the circuit theory and
deep learning are combined, so that the method can be widely applied to the field of
ecological engineering. According to the method, multiple potential migration paths are generated, dependence on a single minimum resistance path is avoided, more diversified migration choices are provided, path weights are dynamically optimized in combination with a
time sequence model, channels with high ecological values and high migration requirements are preferentially protected, and it is guaranteed that a migration network has enough elasticity and adaptability.