Extracting the effective features of bronze inscription image is the key step of inscription recognition. Aiming at the problem of low recognition accuracy of inscription feature extraction method with image as information carrier due to high feature dimension and complex feature vector, an integrated learning and recognition method of inscription graphics based on the dual features of topology and mesh is proposed. Taking graphics as the representation of inscriptions, the proposed method extracts topological features and 7-dimensional text structure graphic features, which effectively describes the structure information of inscription text. On this basis, the proposed method uses the 8-dimensional and 4-direction elastic mesh features of the global and local structure information of the inscription after dimensionality reduction to solve the problem of high dimension of the feature vector caused by the extraction of the image features of the inscription. Finally, taking topological features and elastic mesh features as the feature vectors of integrated learning samples, Bagging method is used to integrate machine learning classifiers with different sensitivity of feature vectors, so as to improve the model training efficiency and recognition accuracy. The experimental results show that compared with the image feature extraction method, the proposed method improves the accuracy of inscription recognition by 15.54 percent, and the dimension of inscription feature vector and running time are greatly reduced.