The recognition of metal fracture images in an industrial environment plays a pivotal role in the analysis of metal failures and carries substantial research significance. While convolutional neural networks have been proven effective in image recognition tasks, the recognition of metal fracture images in an industrial environment still encounters the following challenges. 1)Metal fracture images exhibit strong intra-class complexity and inter-class similarity. 2)Existing CNN structures are complex, with a large number of parameters, which makes deployment on embedded devices challenging. To address the aforementioned problems, this paper proposes a metal fracture image recognition method based on the lightweight CNN. First, a CNN model structure with multi-feature fusion is designed to enhance the network's feature extraction capability. Second, a hybrid pruning algorithm is proposed to slim the network and reduce the complexity of the algorithm. Simultaneously, the search process for important hyperparameters is treated as an optimization problem, and the Bayesian optimization(BO) algorithm is utilized to solve it, thereby automating the model design and pruning process. The experimental results show that the proposed method requires only 3.82 million parameters to achieve 97.56% recognition accuracy. The deployment on the Jetson Nano embedded platform verifies the practical feasibility of the proposed method.