## 1 Introduction  
With Industry 4.0 marked by the full use of industrial Internet of Things, the industrial system architecture has evolved from the previous centralized model to a collaborative cloud- and edge-based distributed system architecture [1]. Consequently, new industrial software has emerged to address the challenges posed by complex industrial application scenarios [2], with a focus on achieving interoperability and reusability. The service-oriented architecture (SOA) system, known for its flexibility, composability, and independence, has been recognized as the optimal solution for this scenario [3]. Therefore, the development of industrial software systems in IoT adopts a service-oriented approach, encapsulating each hardware and algorithm-related functional block into a service component, thereby enabling interoperability and facilitating reusability. This concept of service-oriented componentization represents the future direction of low-code platforms, and serves as a vital theoretical tool for digital transformation in the industrial field [4]. As mentioned earlier, industrial software service components encapsulate hardware functions to provide a flexible and efficient way to implement solutions for industrial tasks. For example, in traditional manufacturing industry, robot arm service components, sensor service components, etc., can be selected for different task requirements in shop floor production scenarios to coordinate the flow of production tasks. In addition, service components avoid the problem of repeated development, effectively reducing development costs, as the same service component can be applied to different scenarios and tasks according to the requirements. Users do not need to have advanced programming knowledge, only to understand the combination of component functions according to the task scenario. The above advantages make service components a powerful tool to meet different task requirements. In this context, the number of industrial software service components is rapidly increasing, and implementing service component recommendations for different tasks to improve component utilization and reduce the development cost of manual selection is becoming a key technical issue. The recommendation domain has long been at the forefront of technological advancement and innovation. Numerous methods have been introduced to enhance the precision of service recommendations. In previous research, these have been broadly classified into two categories, namely, traditional recommendation models and deep learning recommendation models. Traditional recommendation models such as Collaborative Filtering (CF) [5; 6; 7], Matrix Factorization (MF) [8; 9; 10], Logistic Regression (LR) [11], and Factorization Machines (FM) [12; 13; 14] rely on the advantages of strong interpretation, fast training, and low hardware requirements for easy deployment; they are used in a large number of practical scenarios, and form the basis of deep learning models. However, traditional models may not possess strong generalization capabilities, rely more on manual preprocessing, and do not take into account the higher-order relationships between features. To address these shortcomings, deep learning recommendation models have been proposed in the recommendation domain, such as the AutoRec system [15] proposed by the Australian National University. AutoRec integrates AutoEncoder principles with Collaborative Filtering, presenting a neural network recommendation model featuring a single hidden layer. However, its simple structure suffers from a certain lack of expressive power. Google proposed the Wide&deep model [16] in 2016, combines the memory Capability of traditional low-order feature interaction with the generalization capability of deep learning model. This initiated the idea of fusing different network structures.