## 1 Introduction  
In manufacturing, the key to quality control lies in the manufacturing process itself; thus, monitoring and control of the manufacturing process is the focus of manufacturing quality control [1]. The product quality is the result of multiple working steps which are the basic units of the manufacturing process [2; 3]. The mapping of workers, that is, of the process flow of workers' processing actions of the workpiece, consists of multiple separate processing actions [4]. Identifying workers' processing actions and processing action flows can contribute to effectively judging whether the processing status of the workpiece and the processing flow are disordered, making it possible to control the quality of the final product [5]. Monitoring and controlling the quality of processes has become the most important task of quality control. Manual observation is the main method of traditional monitoring, which is time-consuming and labor-intensive. It is increasingly difficult to meet production needs using this method. The emergence and continuous development of intelligent monitoring based on deep learning provides an effective way to monitor processing actions, and is also an important enablingtechnology for the future transformation and upgrading of manufacturing and management to digital and intelligent modes [6; 7]. As the key technology of intelligent monitoring, action recognition has received significant attention in recent years [8; 9; 10]. Its research routes can be divided into two categories: image-based and human skeleton-based recognition methods. * Image-based recognition methods  
[PERSON] et al., analyzed the changes of pixels in video sequences and used the dense trajectories (DT) [11] and improved dense trajectories (iDT) methods [12] to identify actions. [PERSON] et al. proposed a method using CNN (Convolutional Neural Networks) to classify trajectories [13]. [PERSON] et al., used 3D convolution to process temporal and spatial features at the same time [14]. [PERSON] used two-stream CNN, with one stream extracting spatial information from single-frame optical flow and another extracting temporal information from multiframe optical flow [15]. Image-based methods can extract more features from the whole frame, but due to the excessive attention to the background, light and other information of the image, much information is redundant and the method is less efficient and accurate. * Human skeleton-based recognition method  
[PERSON] et al., used the distance between human joints to generate grayscale images and then employed CNN for classification [16]. [PERSON] used 3D human posture data and three different machine learning techniques to recognize human activity [17]. [PERSON] et al. n considered high order features like relative motion to different joints and proposed a novel high order joint relative motion feature and a novel human skeleton tree RNN network [18]; [PERSON] et al. took the skeleton as a graph and used a graph neural network for classification [19].The accuracy of the methods based on skeleton joint information is generally greater than methods based on image recognition, but these methods only consider the movement of the human body, which is too simple to further improve the accuracy of human action recognition. Current research is still overly focused on the recognition of a single action rather than of continuous action processes. However, in a complex workshop production scenario, an analysis of a single action by a worker cannot meet current quality control needs. In many production processes, the continuous flow of workers' actions also needs to follow strict yet flexible process standards [20; 21; 22]. Therefore, it is equally important to analyze and judge the normalization of the action flow. On the other hand, the task of action recognition in a workshop is different from the same task in a natural scenario, because workers' actions mostly involve the use of tools to operate the workpiece or the equipment.