• A multi-objective DG planning model is developed from the perspective of multi-dimensional data mining. • The model is used to solve the correlation analysis for wind speed, light intensity, and load demand of adjacent regions. • The spatiotemporal correlation, economic and system safety and environmental benefits are considered. • The effects of the seasonal difference on the favorable level of the DG's grid connection have been investigated. Aiming at the problem of the distributed generation (DG) planning caused by the strong spatiotemporal coupling between DG output and load demand in adjacent areas, a multi-objective planning model is proposed to describe the spatiotemporal correlation of sources. By combining the most weight supported tree (MWST) and depth first search (DFS), the method achieves the a priori requirement for constructing bayesian network (BN) structure using the K2 algorithm. Then, the MDK2-BN model is established through the measured data, which can describe the correlation between multi-dimensional wind-photovoltaic-load. A DG multi-objective programming model with maximum annual profit rate and minimum comprehensive operation risk is constructed. The results has three main advantages: (1) the MDK2-BN structure can achieve satisfactory results when dealing with small networks. (2) the MDK2-BN model conforms to the spatiotemporal correlation of the DG output, and the proposed configuration can improve the access capacity of DG. (3) the favorable level of the DG's grid connection can be effectively improved by considering the seasonal difference in performance and providing the planners with the decision-making references that balance the economic benefits, system operational safety, and environmental benefits.