Paper Title
Artificial Intelligence-Driven Neural Network Approaches for Enhancing Socio-Labor Management in Smart Megacity Digital Economies

Abstract
Modern megacities are at the epicenter of social and technological transformations, requiring new approaches to human capital management. The rapid digitalization of the economy, the rise of flexible employment forms, and the evolving labor market structure call for intelligent tools capable of responding quickly to urban labor challenges. Neural network technologies are emerging as one of the most promising approaches for analyzing, forecasting, and optimizing socio-labor relations. This study addresses the scientific problem of declining effectiveness of traditional labor management methods amid digital transformation. Increased non-standard employment, remote work proliferation, and high mobility complicate stable labor resource allocation, heightening social tensions and destabilizing labor markets. The research employed scenario analysis, neural network modeling (MLP, CNN, RNN, LSTM), econometric methods, and Big Data and NLP techniques. Data sources included Rosstat, the Ministry of Labor of Russia, job aggregators, urban portals, social networks, and corporate HR systems, covering over 12 million records from Russia’s major cities. Three neural network models were developed: (1) an LSTM-based workforce demand forecasting model improving short-term vacancy prediction by 30%; (2) a CNN-based social tension risk model capable of reducing crisis occurrence probability by 20%; (3) an RNN-based labor redistribution model optimizing vacancy offerings and easing employment service load by 25%. Additionally, digital maturity scenarios for urban labor management were formulated. The results demonstrate that neural network solutions significantly enhance predictive governance of labor relations, offering practical applications for public employment services, municipal management, and private HR platforms. Keywords - Digital Economy, Megacities, Neural Networks, Socio-Labor Relations.