Active distribution network involves a variety of energy forms (such as photovoltaic, wind power, micro gas turbine, battery, etc), the output characteristics of these energy forms are different, and there is a complex coupling relationship between them. Considering the constraints of power consumption and energy storage, it is difficult to obtain the optimal scheduling scheme of multi-energy complementarity of source load and storage. Therefore, a multi-energy complementary cooperative scheduling method for active distribution network is proposed. The output results of power stations in active distribution network are calculated, and the output characteristics of integrated energy after multi-energy complementary are obtained by combining time factors. Based on the original source-load-storage technology, the concept of multi-energy complementarity is introduced, and the optimization goal is to minimize the operating cost and the minimum network loss. At the same time, the adjustment amount of day-ahead scheduling and scheduling units is considered to minimize, and the absorption amount of new energy is strengthened to the maximum extent. The analytic hierarchy process is adopted to optimize the corresponding weight of the target, and the objective function of cooperative optimal scheduling is constructed. In addition, the cold power constraint, hot power constraint, electric power constraint, energy storage constraint and power flow constraint for the cooperation of various energy sources are set, and the particle swarm optimization algorithm is adopted to optimize the cooperative scheduling. The particle swarm optimization algorithm is improved by adjusting the acceleration factor and dynamically adjusting the iterative strategy. The objective function is solved and the multi-energy complementary cooperative optimal scheduling is realized by constantly updating the particle speed and position. The experimental results show that the proposed method significantly improves the consumption of new energy and voltage stability in intra-day optimization scheduling tests, and exhibits high energy utilization efficiency in different geographical, climatic, and energy structure regions, verifying its wide applicability and optimization effect.