<p>Optimizing labor resource allocation and eliminating imbalances are crucial for advancing high-quality full employment. Against the backdrop of industrial digitization, exploring how labor and digital factor inputs shape labor resource allocation efficiency holds significant importance. This study utilizes a three-stage data envelopment analysis model to examine the impact of digital factor inputs on labor resource allocation efficiency, drawing on national and regional panel data from 31 Chinese provinces. It employs the coefficient of variation and static panel models to test for σ-convergence and β-convergence characteristics, and constructs a Markov chain model to investigate the presence of a “Matthew effect” in provincial labor resource allocation efficiency, while also analyzing dynamic trends and regional disparities across China’s four major regions. The findings reveal that after controlling for environmental factors, labor resource allocation efficiency is significantly enhanced under the synergistic input of labor factors and digital factors. However, China’s labor resource allocation efficiency exhibits a pronounced “Matthew effect”—where the strong grow stronger and the weak grow weaker. Targeted policy recommendations are proposed to address this issue across different regions. By examining the measurement and evolutionary characteristics of labor resource allocation efficiency, this study aims to provide valuable insights for improving such efficiency and promoting high-quality full employment in China. </p>