A missing data repair method based on spatio-temporal fusion graph convolutional network was proposed in order to solve the problem of insufficient traffic flow characteristics mining by existing spatio-temporal correlation repair method. Two types of functions were used to respectively calculate the temporal autocorrelation coefficient and spatial correlation coefficient of traffic flow data by analyzing the spatio-temporal characteristics of traffic flow. The deployment position of the traffic detector was used as a node to form a geometric topology graph, and a spatio-temporal fusion matrix was constructed by linear fusion rules, which replaced the adjacency matrix of the graph convolution input layer to capture the fine-grained spatio-temporal relationship of the traffic flow. The lightweight one-dimensional convolution layer was used to learn the temporal characteristics of multi-channel time series vectors in order to speed up the convergence speed of the model. The graph convolutional layer was used to learn the spatial characteristics of traffic flow data. A spatio-temporal fusion graph convolution network repair model was constructed. The experimental results show that the repair accuracy and model convergence speed of the method in multi-detector scenarios were improved compared with other repair methods, which can effectively repair the missing traffic flow data.