In view of the problem that the existing exam paper generation technologies pay too much attention to the difficulty of generated exam papers， while ignoring other related objectives， such as quality， score distribution， and skill coverage， a multi-objective exam paper generation method guided by reinforcement learning and matrix completion was proposed to optimize the specific objectives in the field of exam paper generation. Firstly， deep knowledge tracking method was used to model the interaction information among students and response logs in order to obtain the skill proficiency of the student group. Secondly， matrix factorization and matrix completion methods were used to predict the scores of students' undone exercises. Finally， based on the multi-objective exam paper generation strategy， in order to improve the Q network update efficiency， an Exam Q-Network function approximator was designed to select the appropriate question set automatically for update of the exam paper composition. Experimental results show that compared with the models such as DEGA （Diseased-Enhanced Genetic Algorithm） and SSA-GA （Sparrow Search Algorithm - Genetic Algorithm）， it is verified that the proposed model has significant effect in solving multiple dilemmas of exam paper generation scenarios in terms of three indicators — difficulty， rationality and accuracy. The effect of verifying the models mentioned in the solution of the test papers is significantly effective.