## 1 Introduction  
Protein is an essential element in body activity [1]. In the course of life, proteins are closely linked and interact with each other to form protein-protein interaction (PPI) networks [2]. When some of the essential proteins in the PPI network are removed, it leads to the loss of related functions and physical inactivity [3]. Therefore, the prediction of essential proteins based on PPI networks has a theoretical basis for the exploration of pathogenic genes and drug target development [4]. Early on, the identification of essential proteins mainly occurs through biological experiments [5]. Although biological experimental techniques have high accuracy, such experiments are time consuming and expensive [6]. With the development of information technology, it has become a new trend to predict essential proteins based on protein complexes or topological properties [7]. The topology of the PPI network describes the association of proteins in the form of nodes and edges [8]. Social network research shows that the greater the degree of connection between a node and other nodes, the more the node is important [9]. According to the number of degrees of nodes and the characteristics of PPI network topology, some scholars have proposed many classical algorithms, such as degree centrality (DC)[10], betweenness centrality (BC) [11], closeness centrality (CC) [12], subgraph centrality (SC) [13], eigenvector centrality (EC) [14], information centrality (IC) [15], etc. In addition, some scholars have extended many mixed topological features to identify essential proteins based on degree centricity. This combined method has better identification accuracy than a single measurement method, such as edge clustering coefficient (ECC) combining nodes and edges [16]. The evaluation of essential proteins based on the topological characteristics of PPI networks ignores the biological significance of proteins as carriers of life activities. In addition, the essential proteins identified by these methods suffer from false negatives and false positives. To solve this problem, many algorithms have emerged to predict essential proteins by combining multiple biological information with topological features. For example, gene expression profiles are fused with network topological features [5; 17; 18], PPI networks, subcellular locations are fused with gene expression profiles [19], and so on. Experimental results show that these methods can improve the recognition accuracy of essential proteins. However, due to a large number of protein-related features, how to effectively use these features for essential protein recognition is another problem that needs to be solved. Deep learning (DL) relies on the modeling ability of deep neural networks, which can not only automatically obtain multiple features from original data, but also model the non-linear relationship between features. Since it was proposed, DL has made breakthroughs in image processing and natural language understanding, and has also been widely used in the field of biological information [20; 21; 22; 23]. The advantages of the DL framework in essential protein recognition have been confirmed by many scholars. It can provide good support for learning biological sequence data, capturing topological features from network models, and mapping network nodes into low-dimensional dense vectors [23]. Although DL can discover and characterize the complex structural features of the essential protein recognition process and improve performance, it is time consuming to train and complicated to verify the correctness of the model. At the same time, due to the absence of some data in the biological database, the robustness of the DL-based essential protein prediction method needs to be strengthened. In this paper, we propose a novel DL-based method to improve the accuracy of essential protein recognition.