Community structure and dynamics is the core content of community ecology, and the prediction of population is one way to study the community dynamics. This study attempted to establish a neural network model based on BP neural network model in the rodent communities in Alasan desert area from 2006 to 2014. Through the simulation study and the establishment of the model, BP neural network could achieve the simulation and forecast the dynamic law of the number of rodent communities. Taking Alasan Desert as a test area and the number of rodents as the research object, this study used mark recapture method to monthly monitor the catches from April to October from 2006 to 2014, count the minimum alive number, and set up BP neural network prediction model. Then, built the training network by the data from 2006 to 2013 minimum survival, and used 2014 year′s data for verification and testing. Through comparing hidden layer, double hidden layer and triple hidden layer BP artificial neural network model. The results showed that: 1) When the nodes' number of single hidden layer were 6, the maximum error percentage of single hidden layer model was 16.13%, and the determination coefficient was 0.998 0 (P = 0.006 0, Table 1). 2) When the nodes' number of the two hidden layer were both 6, the maximum error percentage of double hidden layer model was 8.58%, and the coefficient of determination was 0.999 5 (P = 0.002 3, Table 2). 3) When the nodes' number of the triple hidden layer is 1, 10, and 7, the maximum error percentage of triple hidden layer model was 5.87%, and the determination coefficient was 0.999 2 (P < 0.000 1, Table 3). 4) The forecasting effect of the different hidden layer network model has been achieved. By comparing the maximum error percentage, the average error percentage, the decision coefficient, and nonlinear fitting rate, the triple hidden layer was better than other two BP neural network models (Table 4). In this paper, the BP neural network model with triple hidden layer was most suitable for the prediction of rodent population in Alasan desert area.