Research on the Employment Prosperity Index of New Economic Service Industries in China based on Big Data

Abstract

China's new economic service industries, catalyzed by supply-side reforms and the "Mass Entrepreneurship and Innovation" initiative, have become pivotal drivers of economic transformation. This research pioneers a comprehensive framework for monitoring sectoral talent demand through big data analytics. We developed specialized web crawlers to harvest 4.3 million recruitment postings from leading platforms, subsequently applying machine learning algorithms to classify enterprises across 14 policy-defined sectors. Our innovative Employment Prosperity Index (EPINE) methodology quantifies demand fluctuations by analyzing daily recruitment volumes against historical baselines. Empirical analysis reveals sustained talent demand growth throughout 2015-2016, with the composite EPINE index consistently exceeding expansion thresholds. Significant seasonal patterns emerged, showing pronounced November-January peaks contrasted with February and July-September troughs. Sectoral disparities proved particularly striking-energy conservation services and fintech demonstrated robust expansion, while leasing industries exhibited concerning volatility including contraction periods. Competency analysis further identified autonomous learning capabilities and cross-disciplinary expertise as critical talent requirements across all sectors. Validation against established economic indicators confirms EPINE's reliability as a real-time diagnostic tool. The 89% correlation with external indices underscores its utility for policymakers addressing industrial imbalances. While this study demonstrates methodological innovation in labor analytics, future enhancements should prioritize multi-platform data integration to overcome current 75% classification accuracy limitations.