For the difficulty in measuring the distribution characteristics of PM_2.5 concentration in the atmosphere, we used 532 nm lidar to continuously observe the Huainan area from June 1st to December 31st, 2016. A regression prediction model was established concerning the atmospheric boundary layer height, aerosol optical depth, temperature, relative humidity, wind speed, visibility, and measured PM_2.5 concentration to identify the PM_2.5 concentration. Since the traditional backpropagation neural network (BP) was prone to the local minimum, we adopted a genetic algorithm-based backpropagation neural network (GA-BP) according to the data characteristics and applied the genetic algorithm to finding the optimal weights and thresholds, balancing global and local contradictions. A comparison of the two regression models demonstrates that the GA-BP method is significantly better than the BP method. The correlation index R²of the test set and the mean forecast error are respectively 0.623 and 24.692 μg/m ³ for the BP method, and 0.899 and 7.122 μg/m ³ for the GA-BP method. These results indicate that lidar can effectively monitor the PM_2.5 distribution in the atmosphere and provide data support and reference for the monitoring of atmospheric PM_2.5 in the Huainan area.