Abstract: Welding forming quality monitoring is crucial for modern manufacturing industry, but most of the existing quality identification methods are based on single sensor, which makes it difficult to further improve the identification accuracy and has weak anti-interference ability under complex conditions. To overcome the shortcomings of single sensor identification technology, multi-source information fusion technology can make full use of the advantages of different types of sensors to achieve more comprehensive and accurate monitoring of the welding process. However, in the process of multi-information fusion, the feature mining mechanism of the deep learning model still lacks explanation, and the complementarity of different information is still unclear. In this paper, a multi-information hybrid-driven CNN-LSTM welding quality monitoring model is proposed. By fusing image and voltage signals, an average recognition accuracy of 99.72% is achieved. In addition, the visualization results show the complementary advantages between different information.