For improving the accuracy and validity of social E-commerce recommendation services, a trust-aware collaborative filtering recommendation method was proposed with considering the factors that influence the trust relationship of users in social E-commerce, such as transaction evaluation score, transaction frequency, transaction amount, direct trust and recommended reputation. The belief factor was introduced to compute the trust relationship of social E-commerce users, the cosine similarity method was used to calculate the similarity of the users, the harmonic factor was used to synthesize the influence of the trust relationship and similarity on the users, the Mean Absolute Error (MAE), rating coverage and user coverage were used as the evaluation indexes. The experimental results show that the accuracy of the trust-aware collaborative filtering method is superior to the traditional collaborative filtering method and the regularized matrix factorization based collaborative filtering recommendation method in that the MAE reduces to 0.162, and the rating coverage and user coverage rise to 77% and 80% respectively. This proves that the trust-aware collaborative filtering method can solve the problem of recommending the commodities with less transaction evaluation.