The invention provides a graph neural network GAT-based patent valuation method and system for a click auction website, and relates to the field of evaluation methods. The weights of the patent attribute parameters in a training data set are trained by using an attention graph neural network GAT, a patent valuation model is abstracted into a knowledge graph, an assessed value of the patent value is taken as a Root of the graph, an attribute parameter related to the patent valuation is taken as a neighbor node of the root, and an edge between the Root and the Node represents a weight coefficient W between the parameter and the assessed value; and the weight coefficient W is trained by using the GAT, after W is trained, a test data set is input, and the evaluation value of each patent data in the test data set can be obtained by multiplying the value of each attribute parameter node in the test data set by the corresponding weight. The weight value is obtained by utilizing big data, andthe value of the patent is integrally evaluated and confirmed, so that the value of patent evaluation is more accurate.
Graph neural network GAT-based patent valuation method and system for click auction website