Multiple-choice reading comprehension typically adopts the two-stage pipeline framework of evidence extraction and answer prediction, and the effect of answer prediction highly depends on evidence sentence extraction. Traditional evidence extraction methods mostly rely on phrase matching or supervise evidence extraction with noise labels. The resultant unsatisfactory accuracy significantly reduces the performance of answer prediction. To address the above problem, this study proposes a multiple-choice reading comprehension method based on multi-view graph encoding in a joint learning framework. The correlations among the sentences in the text and those of such sentences with question sentences are fully explored from multiple views to effectively model evidence sentences and their relationships. Moreover, evidence extraction and answer prediction tasks are jointly trained so that the strong correlations of the evidence with the answers can be exploited for joint learning, thereby improving the performance of evidence extraction and answer prediction. Specifically, this method encodes texts, questions, and candidate answers jointly with the multi-view graph encoding module. The relationships among the texts, questions, and candidate answers are captured from the three views of statistical characteristics, relative distance, and deep semantics, thereby obtaining question-answer-aware text encoding features. Then, a joint learning module combining evidence extraction with answer prediction is built to strengthen the relationships of evidence with answers through joint training. The evidence extraction submodule is designed to select evidence sentences and fuse the results with text encoding features selectively. The fusion results are then used by the answer prediction submodule to complete the answer prediction. Experimental results on the multiple-choice reading comprehension datasets ReCO and RACE demonstrate that the proposed method attains a higher ability to select evidence sentences from texts and ultimately achieves higher accuracy of answer prediction. In addition, joint learning combining evidence extraction with answer prediction significantly alleviates the error accumulation problem induced by the traditional pipeline framework.