Abstract: Engineering problem-solving involves multidisciplinary knowledge, and the users' knowledge needs cannot be satisfied by offering single filed knowledge. Therefore, an engineering knowledge recommendation method, topic diversity collaborative filtering (TDCF), was proposed to optimize the topic diversity of the recommendation list. In TDCF, a knowledge topic network was first constructed in the form of a directed graph, and the user's profession on the knowledge topic was calculated. Then a rating matrix pre-filling algorithm considering the user's profession was developed to alleviate the sparseness of the rating matrix. Finally, the knowledge topic network and user's profession were used to improve the user similarity calculation method in the traditional collaborative filtering recommendation algorithm, and thereby the diversity of recommendation results can be improved in a targeted manner and the recommendation accuracy and user satisfaction can be promoted. Experiment based on the real data collected from a domestic shipyard showed that TDCF's recommended results achieve higher F1-Score and diversity, which are 0.52 and 0.44 respectively, thus outperforming other benchmarking algorithms. Therefore, TDCF is feasible and superior, and can provide better knowledge recommendation services.