## 2 Related Work  
The first path inference algorithm, the path ranking algorithm (PRA), was developed in 2010 by [PERSON] et al. [8]. The PRA is based on a random walk strategy to complete a search process for the multihop connection paths between entities. Then, [PERSON] et al. [9] proposed DeepPath methods for KG reasoning. In the same year, [PERSON] et al. [10] proposed the MINERVA reasoning algorithm based on KGs and enhanced learning. The earliest feature-based model, TransE [11], was proposed in 2013, as well as knowledge graph entities and relationships in KGs to low-dimensional vector spaces. On the basis of TransE, other authors have proposed successive methods, such as TransH [12], TransD [13], TransR [14], and TransSparse [15], which indicates the effectiveness of this idea. [PERSON] et al. [16] proposed a KG inference model based on a compound vector space. [PERSON] et al. [17] proposed a chain-of-reasoning model. [PERSON] et al. [18] proposed a knowledge map question-and-answer model based on variational reasoning in 2017. A few months after the DIVA model was proposed, [PERSON] et al. [7] proposed a single-sample knowledge graph reasoning model for a single-hop relationship. Compared with the DIVA route that focuses on extracting paths and learning path features, the model directly compares the structural features of the knowledge graph spectrum subgraph with the target entity as the core to realizing the ability of single-sample learning. Although the structure is very simple, it has a greatly improved performance compared with the previous DIVA model structure diagram. CNNs were designed only for continuous data with regular sizes, such as image and voice data. The discrete and irregular characteristics of graph structure data are quite different. Therefore, the research community has been exploring how to apply CNN ideas to graph structure data [19]. In 2015, [PERSON] et al. [20] introduced a CNN to graph data to extract molecular structure features, but this method is used only for molecular structure graphs and lacks a systematic definition. In 2017, [PERSON] and [PERSON] [21] systematically proposed a graph convolutional network (GCN) and, for the first time, defined the convolution operation on graph structure data utilizing single-sample learning methods. [PERSON] et al. [22] proposed a knowledge graph reasoning model based on the attention mechanism GCN graph convolution network in 2019. The author used GCN to convolute the original graph structure directly, extract the topology information of the training sample in the knowledge graph, and finally generate the feature vector and predict it. In the experiment, the performance of the model using GCN for feature analysis was significantly improved compared with the traditional feature-based method. The model can only be trained on a fixed network structure and needs to be retrained after the network structure changes. It has poor adaptability to more dynamic graph data. In 2021, [PERSON] et al. [23] proposed a new path-based reasoning method, with k-nearest neighbor and the position embedding method. This method can improve upon path reasoning methods that ignore both the overlapping phenomenon of paths between similar relations and the order information of relations in paths. [PERSON] et al. [24] integrated a graph self-attention (GSA) mechanism to capture more comprehensive entity information from the neighboring entities and relations. They trained the agent in one pass to reduce the problem complexity. [PERSON] et al. [25] proposed the Deep-IDA framework, which applies neural networks and reinforcement learning (RL) to empower the IDA algorithm to tackle the path discovery problem in KG reasoning. In 2022, [PERSON] et al. [26] proposed a dynamic knowledge graph reasoning framework. They used the long short-term memory (LSTM) and the Markov decision process to efficiently model the path reasoning, which incorporates the embedding of actions and search history into the feed-forward neural network as the policy network.