Objective To construct an ensemble neural network model to achieve traditional Chinese medicine (TCM) syndrome classification for rheumatoid arthritis (RA) and explore the importance of the features and risk factors. Methods An ensemble neural network model FEN (feature extration network) was proposed to solve the issues such as poor label correlation and low generalization performance in multi-label classification of TCM syndromes of RA based on artificial intelligence technology. The FEN model utilized a feature extraction classifier based on deep neural network to extract deep features from clinical multi-label RA samples, enhancing the discriminative power of RA features. By measuring label correlation based on covariance theory, the input space of the classifier chain was adjusted to reduce the spread of RA error information and redundancy. An ensemble learning method was used to mitigate the impact of unreasonable label sequences in the classifier chain on RA feature classification. Additionally, the importance of main and accompanying TCM syndrome features of RA was analyzed and potential risk factors were explored. Results The FEN model had excellent performance in a 10-fold cross-validation, with Hamming loss, one-error, accuracy, and F1-score being 0.003 6, 0.024 8, 97.52%, and 99.18%, respectively. Compared with 7 typical multi-label classifiers (classifier chain, label powerset, binary relevance, random k-labelsets, multi-label K-nearest neighbor, ensemble classifier chain, and ensemble binary relevance), the FEN model had better classification capabilities. The analysis of feature contribution indicated that the features of main and secondary symptoms might be used as important indicators of classification of TCM syndromes of RA, and were the main factors affecting the classification of main and accompanying syndromes. Conclusion The RA TCM syndrome classifier based on ensemble neural network has high classification accuracy and efficiency, providing important reference for the clinical diagnosis and treatment of RA.