With the development of distribution network and new energy, distributed generation (DG) is widely used. The strong intermittency of DG, the spatial distribution characteristics of the distribution network, and the temporal sequence of the distribution network electrical quantities lead to the difficulty of achieving optimal operation of the distribution network in reactive power optimization problems by the traditional optimization methods and optimization algorithms that consider a single dimension. In this context, the spatial distribution characteristics and time series characteristics of distribution networks are taken into consideration at the same time, and a multi-time scale distribution network reconstruction and reactive power collaborative optimization method based on adaptive graph attention network is proposed, spatial correlation modeling is performed based on the adaptive adjacency matrix and graph convolution network (GCN) with improved spatial attention mechanism, multi-time scale partitioning and bidirectional gated recurrent unit (Bi-GRU) optimization is adopted for time correlation modeling, and an overall optimization model is obtained; at the same time, the reconfiguration of the distribution network is considered containing distributed power sources in concert with reactive power collaborative optimization, and the traditional reactive power device and the soft open point(SOP) for collaborative optimization are utilizd. Finally, experiments and validation are carried out through the improved IEEE 33-bus distribution system, and the results show that the proposed method in this paper reduces the average loss by 55.83 % under different penetration rates, which is 10.1 % lower than the deep learning model, and ensures that the overall voltage level fluctuation per hour is less than 0.005 5 p.u., which verifies the validity and accuracy of the proposed model in reactive power optimization problems.