Expert recommendation is a research hotspot in the field of recommendation system. The rationality of expert information feature extraction directly affects the accuracy of recommendation. However， most expert recommendation methods donot build text graphs of feature relation for multi-source information， and ignore the correlation between attribute features. Additionally， most expert recommendation methods cannot expand the features of knowledge field according to the relevance of text graph. Therefore， we propose CMFBG， an expert recommendation method combining multi-features and bi-directional graph classification. Specifically， CMFBG obtains multi-feature information of experts through multi-source information fusion， and construct text graphs for different attribute features within categories. Then， CMFBG employs bidirectional encoder representation from transformer （BERT） and graph convolutional network （GCN） models to extract features and fuse them. Finally， CMFBG employs the bidirectional attention mechanism to enhance the extension of the source data to the graph features and realize the classification of the graph structure. The experimental analysis on the same expert data set shows that the precision of CMFBG is 91.71% higher than other algorithms in the task of graph classification.