Typically, the options of multiple choice Machine Reading Comprehension (MRC) are not directly extracted from the given document. Thus the answers need to be summarized or rewritten or even inferred from document or from the world’s knowledge. Most existing models adopt attention mechanism to generate an interactive representation of document, question, and option. However, these models are limited by only using the given document rather than common knowledge, leading to poor result when dealing with questions requiring external knowledge assistance reasoning. To address questions requiring external knowledge assistance reasoning, we propose a novel neural model by integrating external commonsense knowledge to assist multi-step reasoning. Our model first interacts information among document, question, options, and related external knowledge by attention mechanism and then predicts answer by multi-step reasoning through the interaction results. The experimental results on the SemEval-2018 MCScript corpus show that the proposed model improves the accuracy of question answering requiring common knowledge reasoning.