The current integration of multi perspective recommendation nodes in pharmaceutical ideological and political textbooks is generally independent, with limited recommendation scope, resulting in an extension of unit target recommendation time. This article proposes a multi perspective recommendation method based on clustering algorithm for the integrated design and analysis of pharmaceutical ideological and political textbooks. According to the actual recommendation requirements and standards, Collaborative filtering pre-processing is first carried out for the ideological and political information integration of the medical profession. Adopting multi-level methods to break the limitations of recommendation scope, multi-level personalized recommendation recognition and analysis nodes have been deployed. On this basis, a multi perspective data recommendation structure was constructed and a multi perspective recommendation model for textbook clustering calculation was established. Regularization processing is used to implement data recommendation. The test results show that for three datasets, by studying five sets, the recommendation time of unit targets is better controlled below 0.2 s. This indicates that with the help and support of clustering algorithms, the current resource recommendation effect is better, the recommendation is more targeted, and has practical application value.