A Social Financial Text Classification Method Based on Dynamic Word Embeddings and Semantic Features Fusion

With the advancement of information technology, social networking platforms have been widely integrated into people's daily lives, significantly altering their lifestyle and consumption habits. Currently, these platforms are abundant with texts possessing economic attributes, known as social finance texts. The ability to timely and accurately identify social finance texts with potential value holds considerable economic and practical significance. Addressing the existing challenges: (1) the presence of key information of varying lengths and sparse text features in texts, where single‐layer text content fails to provide sufficient semantic information; (2) the deficiencies of traditional static language models in expressing text features; (3) the cluttered content leading to unclear text themes, thereby diminishing the classification effectiveness. This paper proposes a social finance text classification method based on the DLMFSE (Dynamic Language Model with Multi‐Feature Fusion and Semantic Enhancement) algorithm. It involves semantic feature training of texts through a pre‐trained model based on dynamic word vector representation, obtaining document‐level and character‐level feature vectors. Subsequently, the MKC and Bi‐GRU networks are utilized for multidimensional feature extraction from character‐level vector representations of texts, acquiring multidimensional shallow features and deep semantic features of social finance texts, and merging them with document‐level feature vectors to obtain a multidimensional vector representation of social finance texts. A semantic feature extension algorithm is introduced to extract and expand key text features, resulting in semantically enhanced word vector representations. By integrating the aforementioned vector representations, a multidimensional semantic feature vector for social finance texts is constructed. Experimental results demonstrate that our DLMFSE model achieves a macro‐ F 1 score of 94.2% on a self‐constructed dataset, outperforming strong baselines including BERT by 2.9%. This significant improvement not only validates the effectiveness of our multi‐feature fusion strategy but also indicates substantial practical value for real‐time financial monitoring and opportunity discovery in social media platforms.