## 5 Conclusions  
In this paper, we proposed a relational classification model based on concatenated word vector attention and feature stitching. The model operates by cascading attention in the word vector dimension. This amplified the effectiveness of positive features in relation classification tasks, while suppressing the impact of negative features on the performance of relation classification models. In the feature splicing module, entity features and sentence features were fused to obtain feature vectors with multiple scales. The model can be made to learn the relationship between two entities in many ways. The experiments proved that the above two modules can promote the performance of the relational classification model. At the same time, experiments on the self-labeled standard text dataset and two public datasets, SemEval-2010 Task 8 and KBP37, showed that this model was superior to the existing models. The F1-values reached 78.8%, 89.75%, and 70.28%, respectively.  
In addition, we believe that the relationship classification model based on cascaded word vector attention and feature splicing still has certain problems. During entity extraction, mask matrix generation can only be performed based on the labels of two entities. This limits the model to the recognition of relationships between two entities. This model also has certain limitations when dealing with the task of relation classification when entities overlap. The current model can only classify the relationship between two entities. When the relationship between Entity 1 and Entity 2 is Relationship 1, and the relationship between Entity 1 and Entity 3 is Relationship 2, the model can only recognize one of the relationships, and the other relationship is missed. This is the limitation that mask matrices can only be generated from entity markers. On the one hand, when there are three or more entities in the text or there are multiple relationships between entities, the recognition accuracy of this model was low and cannot be compared with existing models. On the other hand, on other specific domain datasets, data reading and model adjustment need to be performed according to the characteristics of the domain data. The relationship classification model proposed in this paper may not be ideal for other domain datasets.  
In future work, we will build on this model to explore the extraction and classification of entity relationships in texts with multiple entities and multiple relationships. At the same time, introducing external features is also a hot research direction in the field of natural language processing. We will also continue to study how to introduce external features into relation classification tasks to improve the performance of relation classification models. At the same time, we will focus our research on practical applications. Based on artificial intelligence models, we will design and develop relevant software and systems that can be applied.  
**Author Contributions:** Software, resources, methodology, formal analysis, writing--original draft preparation, data curation: X.L.; methodology, validation, formal analysis, data curation, writing--original draft preparation: J.T.; software, visualization N.N.; project administration, funding acquisition J.L.; writing--review and editing J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received support from the National Key R&D Program (2022 YFF0608000), the President's Fund Project of the China National Institute of Standardization (572022Y-9397), and the Science and Technology Project of the State Grid Corporation of China (5700-202241437A-2-0-ZN).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.