Research on entrepreneurship incubation system and mechanism in Guangdong universities and colleges: Application and Optimization Path of Machine Learning Technology

Abstract

To address the core issues of "resource mismatch", "delayed efficiency evaluation"and "insufficient industrial collaboration"in university entrepreneurship incubation in Guangdong, this study constructs an integrated research framework of "data-driven - model evaluation - dynamic optimization"by integrating machine learning and intelligent optimization methods under the guidance of the entrepreneurial ecosystem theory. Based on multi-source official data from Guangdong Provincial Department of Education, Department of Science and Technology, enterprise-side institutions, and cross-system platforms, 22 evaluation indicators across 5 dimensions (government empowerment, university cultivation, industrial collaboration, technical support, and achievement transformation) are selected. The Random Forest (RF) algorithm (n-estimators=120, max-depth=10, min-samples-split=6) is used to rank feature importance, identifying key influencing factors such as industrial demand matching degree (weight=0.35) and university research funding input intensity (weight=0.29). A Long Short-Term Memory (LSTM) network evaluation model is built (22 nodes in the input layer, 64 nodes in each of the 2 hidden layers, 1 node in the output layer, learning rate η=0.001, 800 iterations), and its robustness is tested using Grey Relational Analysis (ρ=0.5) and cross-system data. An improved Particle Swarm Optimization (PSO) algorithm (60 particles, 150 iterations, c1=c2=2.1) is introduced, with the addition of a regional cost elasticity coefficient to design a dynamic resource allocation mechanism. Empirical results show that the Mean Squared Error (MSE) of the LSTM model converges to 0.021 in the training set, with an accuracy of 91.2% in the test set; the external validity reaches 92.3% after verification with data from the industrial and commercial administration and intellectual property bureau. After optimization by the improved PSO, the technology conversion rate of university incubation projects in Guangdong (29.3% in 2024) increases by 13.6%, the resource mismatch rate decreases by 18.5%, and the decline in marginal benefits of subsidies in northern Guangdong narrows from 30% to 12%. This study provides an operable and quantifiable technical solution for the intelligent transformation of the system and mechanism of university entrepreneurship incubation in Guangdong.