Due to the insufficiency of few shot charges and the similarity of case descriptions for the confusing charges, the prediction performance of the existing methods for few shot charges and confusing charges is not promising. To address the forementioned drawbacks, a novel few shot and confusing charges prediction method is proposed, which is based on bi-direction mutual attention mechanism with the auxiliary sentences of case. For the proposed model, firstly, the auxiliary sentence of case via the judicial field is constructed, where the auxiliary sentence of case is considered as external knowledge for mapping the description of the case to the corresponding charge. Secondly, the multi-granularity characteristics of case description and the auxiliary sentence of case are extracted at the level of both word and character, respectively. At the same time, the auxiliary sentence of case and case description are used to build bi-direction mutual attention. Finally, the tendency representation of the case description with the guidance of the auxiliary sentence of case are derived, which improve the prediction accuracy of few shot and confusing charges. The experimental results conducted on the benchmark data of criminal cases show that the proposed model increases the F1 value and prediction accuracy by 13.2% and 4.5%, respectively, and increases the F1 values for the few shot charges and confusing charges by 4.3% and 8.2%, respectively, which significantly enhance the prediction performance for few shot and confusing charges.