National parks are widely recognized as a key spatial conservation strategy for simultaneously safeguarding biodiversity and sustaining ecosystem services, yet comprehensive and causally robust evaluation frameworks are still needed to accurately assess their effectiveness and support evidence-based management. This study evaluates the conservation effectiveness of Qianjiangyuan National Park (QJYNP) from 2015 to 2024 using a multidimensional index, a PSM-DID quasi-experimental framework, and interpretable machine learning. The results show that the direct policy effect was significantly positive during 2015–2020, but shifted to a negative cumulative effect by 2024. The spillover effect in the buffer zone also turned significantly negative, potentially associated with tourism-related development shifting outward. In addition, slope, temperature, and population density were identified as key drivers of EEI heterogeneity with nonlinear threshold effects, while road-related impacts intensified over time. These findings indicate that quasi-experimental approaches better capture phased policy effects than conventional descriptive comparisons, and suggest that simple boundary controls are insufficient; instead, buffer zones should be incorporated into integrated management frameworks to mitigate external development pressures.