Application of Association Rule Mining Algorithm in Talent Training Assessment in Universities Under Social Sustainability

Abstract

From the contradiction between the difficulty of employment in enterprises and the difficulty of employment in college students, we can see that the strategies of cultivating talents in colleges and universities are biased. This research combines the talent cultivation mode of colleges and universities with the current situation of social development, constructs a comprehensive quality assessment system for students, and uses hierarchical analysis and expert opinions to determine the weight coefficients of each index. In response to the shortcomings of traditional mining algorithms, an improved APRIORI algorithm based on the bit-array matrix is proposed to improve the mining efficiency by introducing the duplicate terms of identification and variable records in data compression. The experimental results show that the improved algorithm can achieve fast convergence on different data sets. The minimum running time of BM Apriori algorithm in different nodes is 102s; under different supports, the average running time of BM Apriori algorithm is 1.49s, and the efficiency is improved by 81.4%. BM Apriori algorithm is used for data mining, and the association rules between courses and students’ grades obtained show that students’ grades are prone to decline during the freshman to sophomore years. Schools should focus on cultivating students’ good learning habits, cultivating excellent talents, and promoting the sustainable development of society.