The residual subsidence caused by mining has a long stabilization time and great potential harm. It is necessary to accurately predict the residual subsidence value of the ground of the mining area. In view of the large modeling error and weak applicability of the traditional residual subsidence Verhulst model, the first data of the data sequence is kept unchanged in the modeling process, which leads to the poor prediction effect. Based on the direct discrete Verhulst model, the PSO (particle swarm optimization) algorithm is introduced to find the optimal solution of the initial value of the model iteration, and the direct discrete Verhulst model of mining residual subsidence based on PSO is established. The surface residual subsidence monitoring data sets of two time scales in Yangquan, Shanxi and Yanzhou, Shandong are used for example verification. Finally, the visualization of the model algorithm is realized by using Matlab App Designer tool. The results show that the prediction accuracy and stability gain of residual subsidence in mining area based on direct discrete Verhulst model optimized by particle swarm optimization are obvious, and the developed calculation tool is correct and effective.