[Purpose/Significance] This paper aims to develop a method for measuring the interdisciplinary nature of Chinese scientific research collaborations and to systematically analyze the patterns by which the characteristics of research collaboration organizations influence the interdisciplinary nature of knowledge outputs. Accurate measurement of interdisciplinarity is a prerequisite for evaluating collaborative research quality, optimizing research resource allocation, and advancing interdisciplinary research policies. A framework tailored to Chinese-language literature also helps address the limited applicability of existing approaches, which have primarily been developed for English-language databases. [Method/Process] The study proposed a method for measuring the interdisciplinary nature of Chinese research outputs based on the Chinese Library Classification (CLC) system, which was applicable to large-scale literature datasets and helped mitigate potential endogeneity concerns compared to citation-based measurement methods. Specifically, CLC codes were mapped to disciplinary categories at different levels to identify disciplinary combinations and calculate the interdisciplinarity of individual papers. Using a sample of 124 746 collaborative papers published in 62 CSSCI journals between 2013 and 2022, the study conducted empirical tests with a Tobit model incorporating year and journal fixed effects. [Result/Conclusion] The study finds that, after controlling for year and journal fixed effects, the number of funding grants, internal team size, and the scale of external collaboration all exert significant inverted U-shaped effects on the interdisciplinary nature of research outputs. At moderate levels, larger teams, broader institutional participation, and increased funding support expand access to heterogeneous knowledge, skills, and resources, thereby promoting knowledge recombination. However, once collaboration exceeds an appropriate threshold, rising communication, coordination, and organizational costs inhibit interdisciplinary knowledge integration. These findings remain robust after removing extreme values and using alternative measurement specifications. The proposed method provides a feasible tool for measuring the interdisciplinarity of large-scale Chinese research outputs, while the empirical findings offer practical guidance for configuring research teams, coordinating interorganizational collaboration, and allocating funding support.