With the widespread use of Internet, an increasing number of users are inclined to share personal details and emotion on social platforms. These online text data often capture genuine expressions in various contexts, reflecting the users’ internal psychological traits and personality tendencies. In recent years, research on personality detection based on social media text has made significant progress. However, most researchers rely on unprocessed public datasets, which inevitably contain noise due to their collection process. In addition, there is an over-reliance on semantic features extracted by pre-trained models, with insufficient attention to psycholinguistic features. To address these issues, this study proposes a novel method for personality detection. First, a plug-and-play data cleaning module based on confident learning is used to remove noisy data and improve dataset quality. Second, multi-level psycholinguistic features are extracted to complement the semantic features of the text. The proposed method is evaluated on the public Kaggle MBTI dataset, with results showing that, compared to existing advanced methods, it achieves improvements of 5.48% in accuracy and 4.22% in F1-score.