Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee to form the patentee’s features, and the patent text features are extracted by BERT-DPCNN deep learning model, which is supplemented to the basic patent indicator system. Finally a machine learning algorithm is used to assess the value of patents. Experiment results show that our method can identify high-value patents more effectively and accurately.