The service descriptions provide limited information about application scenarios, creating a gap between Mashup service component Web API recommendations based on functional similarity calculation and desired expectations. Consequently, there is a need to enhance the accuracy of function matching. While some researchers utilize collaborative associations among Web APIs to enhance recommendation compatibility, they overlook the adverse effects of functional associations on Mashup service creation, thereby limiting the enhancement of recommendation diversity. To address this issue, this study proposes a Web API recommendation method for Mashup service components that integrates latent related words and heterogeneous association compatibility. The study extracts latent related words associated with application scenarios for both Mashup requirements and Web APIs, integrating them into the generation of function vectors. By enhancing the accuracy of functional similarity matching, it obtains a high-quality candidate set of Web API components. Function association and collaboration association are modeled as heterogeneous service association. The study utilizes heterogeneous association compatibility to replace collaboration compatibility in traditional methods, thus enhancing the recommendation diversity of Web APIs. In comparison, the proposed approach demonstrates improvements in evaluation indicators, with Recall, Precision, and NCDG enhanced by 4.17% to 16.05%, 4.46% to 16.62%, and 5.57% to 17.26%, respectively. Additionally, the diversity index ILS is reduced by 8.22% to 15.23%. The Recall and Precision values for cold-start Web API recommendation are 47.71% and 46.58% of those for non-cold-start Web API recommendation, respectively. Experimental results demonstrate that the proposed method not only enhances the quality of Web API recommendation but also yields favorable results for cold-start Web API recommendations.