The invention discloses an attribute reasoning and product recommendation method based on an adaptive graph convolutional network. The method comprises the following steps: 1, constructing heterogeneous data: a scoring matrix of a user to a product, a user attribute matrix, a product attribute matrix, a user attribute index matrix and a product attribute index matrix; 2, performing missing value filling preprocessing on the user attribute matrix and the product attribute matrix; 3, obtaining a cooperative matrix through one-hot coding; 4, constructing a feature fusion layer according to the attribute matrix and the collaborative matrix; 5, carrying out feature propagation through a graph convolution layer; 6, constructing a prediction layer to perform attribute reasoning and product recommendation; 7, updating the node attribute matrix according to the output result of the prediction layer; and 8, repeating the steps 4-7 until the attribute reasoning and product recommendation effectsare optimal. According to the method, the high-order structure information of the graph, the internal interaction between the node attributes and the potential association between the node attributesand the link relationship can be fully mined, so that more accurate attribute reasoning and product recommendation are realized.