The invention discloses a cold-start recommendation method based on user preferences and trust. The method comprises the steps of S1, measuring comprehensive trust values between users according to social information of the users, and constructing a trust relation matrix; S2, calculating preference similarity degrees of the users according to user scoring data, and constructing a preference relation matrix; S3, utilizing a calculation method of comprehensive similarity degrees to fuse preference relations and trust relations, and using a bee colony algorithm to iteratively update weights in the comprehensive similarity degrees, carrying out multi-objective optimization to enable the weights to become optimal in a self-adaptive manner, and constructing a preference trust relation matrix; S4, selecting a most-trusted neighbour set of the target user to predict scoring values of corresponding items for the target user on the basis of the preference trust relation matrix; and S5, recommending the items with high prediction scores to the target user. According to the method, the precision of user trust measuring is improved, the user behavior preferences are more accurately constructed, and the quality of recommendation for the cold-start user is improved.