Abstract: With the widespread adoption of social networking platforms,users increasingly express their personal opinions,emotions,and attitudes through text content.These social texts not only carry linguistic information but also implicitly reflect users' behavioral patterns and personality traits.As a fundamental component in areas such as user profiling,personalized recommendation,and mental health analysis,personality recognition has gained increasing research attention.However,current methods still face challenges such as insufficient accuracy and limited model generalization when processing unstructured textual data.To improve the accuracy and efficiency of personality recognition in social texts,a personality trait recognition method based on data mapping and deep learning is proposed.This method firstly introduces a dataset mapping algorithm to effectively unify the feature space of multi-source data and alleviate issues related to inconsistent sample distributions.In terms of model design,multiple mainstream pre-trained language models(such as BERT,RoBERTa,and ERNIE) are fine-tuned to deeply extract personality-related cues from the semantic level of the text.Experiments conducted on a standard social text dataset demonstrate that the ERNIE model achieves the best performance,with an accuracy of 89.942% and an F1 score of 88.576%,significantly outperforming other models.These results validate the effectiveness of multi-source data integration and deep semantic modeling in personality recognition tasks.The proposed method enhances classification performance and provides technical support and methodological reference for future research and practical applications in personality modeling.

Key words: MBTI personality recognition, Data fusion, Deep learning