Power flow calculations are the basis for the operation and control of power systems. In order to solve the problems of uncertainty of voltage fluctuation at the point of load caused by the increasing penetration rate of renewable energy in the distribution network, and the inaccuracy of power flow calculation caused by the insufficient power flow data collection capacity of traditional power system. In this paper, a data-driven power flow analysis model is proposed, and a power flow calculation method based on back propagation neural network combined with genetic algorithm and adaptive moment estimation optimization algorithm is constructed to analyze the power flow calculation method of distribution network under randomness. Firstly, the initial power flow information, topological structure characteristics and power factor indicators are introduced to construct the training set, and the mapping relationship between node voltage and power is fully explored through the training of the regression model. Secondly, the GA-ADAM algorithm is used to optimize the initial value and weight parameters of the model. Finally, based on the IEEE-33 bus distribution network model, the maximum error is 3.93×10-3， average absolute error is 1.46×10-3， and root mean square error is 1.81×10-3 of the model power flow calculation in this article, the optimized BPNN power flow calculation voltage error value is reduced by 37.66%. The simulation results of actual examples show that compared with other methods, the model constructed in this paper has smaller error indicators and higher accuracy, which improves the efficiency and accuracy of power flow calculation.