In the context of sustainable manufacturing, efficient collaboration between humans and machines is crucial for improving assembly quality and efficiency. However, traditional methods for action recognition and human–robot collaborative assembly often face challenges such as low efficiency, low accuracy, and poor robustness. To solve such problems, this paper proposes an assembly action-recognition method based on a hybrid convolutional neural network. Firstly, an assembly action-recognition model is proposed using skeletal sequences and a hybrid convolutional neural network model combining Spatial Temporal Graph Convolutional Networks (ST-GCNs) and One-Dimensional Convolutional Neural Networks (1DCNNs) to sense and recognize human behavior actions during the assembly process. This model combines the joint spatial relationship and temporal information extraction ability of the ST-GCN model with the temporal feature extraction ability of the 1DCNN model. By incorporating Batch Normalization (BN) layers and Dropout layers, the generalization performance of the model is enhanced. Secondly, the model is validated on a self-constructed dataset of assembly actions, and the results show that the recognition accuracy of the model can reach 91.7%, demonstrating its superiority. Finally, a digital workshop application system based on digital twins is developed. To test the effectiveness of the proposed method, three sets of control experiments were designed to evaluate both objective and subjective aspects and verify the feasibility of the method presented in this paper. Compared with traditional assembly systems, the proposed method optimizes the recognition of human–robot collaborative assembly actions and applies them to intelligent control systems using digital-twin technology. This intelligent assembly method improves assembly efficiency and saves assembly time. It enables efficient and sustainable collaboration between humans and robots in assembly, leading to a positive and sustainable impact on the manufacturing industry.

With the constant exploration and development of intelligent manufacturing, the concept of digital twins has been proposed and applied. In view of the complexity and intellectualization of virtual workshop systems, real workshops can link with virtual workshosp based on AR under the structure of digital twins, which allows users to interact with virtual information and perceive the virtual information superimposed on the real world with great immersion. However, the three-dimensionality of virtual workshops and interaction with complex workshop information can be challenging for users. Due to a shortage of input bandwidth and the nontraditional mode of interaction, a more natural interaction technique for virtual workshops is required. To solve such problems, this paper presents a technical framework for 3D eye movement interaction applied to a virtual workshop. An eye movement interaction technique, oriented to implicit interaction and explicit interaction, is developed by establishing behavior recognition and interaction intention understanding. An eye-movement experiment verifies the former’s accuracy is above 90% and had better recognition performance. A better feature vector group of the latter is selected to establish a model and verify its feasibility and effectiveness. Finally, the feasibility of the framework is verified through the development of an application example.