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First, a financial transaction feature extraction model based on DNN is constructed, which can automatically learn the deep features of transaction data and provide strong data support for subsequent anomaly detection. At the same time, the research introduces the knowledge graph technology and constructs the knowledge graph of financial transactions, including transaction entities, relationships, and attributes. By fusing DNN feature extraction results with structured information in a knowledge graph, a new anomaly diagnosis algorithm is proposed. Experimental results show that, compared with traditional deep learning models, the proposed DNN-KG fusion method in this study has achieved significant improvement in [Formula: see text] value and recall rate. Especially in the complex transaction pattern and fraud identification, the DNN-KG method shows stronger diagnostic ability and stability. The effects of different training rounds on the model performance were also analyzed, and it was found that the DNN-KG method could maintain high diagnostic accuracy in different training stages.<\/jats:p>","DOI":"10.1142\/s0218126625504031","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:59:21Z","timestamp":1751950761000},"source":"Crossref","is-referenced-by-count":0,"title":["A Financial Transaction Anomaly Diagnosis Model Based on Deep Neural Network and Knowledge Graph Fusion"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9289-9896","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"Guangzhou City University of Technology, Guangzhou 510000, P. R. 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