Analysis of Factors Influencing the Quality of Mechanical Engineering Applied Talent Training Based on Improved FPMAX Algorithm
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Abstract
In the context of manufacturing transformation and the cultivation of new quality productive forces, the training quality of applied talents in mechanical engineering directly determines the development of intelligent manufacturing and advanced industrial systems. However, the current training system faces structural problems such as complex influencing factors, unclear identification of key driving factors, and insufficient targeted optimization paths. Traditional statistical analysis methods are difficult to uncover potential correlations, resulting in suboptimal resource allocation and a lack of empirical precision in improving training quality. The FPMAX algorithm effectively extracts maximal frequent itemsets from multidimensional data, providing a robust technical framework for analyzing complex variables that influence the quality of applied engineering talent training. By isolating key association rules, the algorithm supports targeted optimization of practical teaching, enterprise participation and training-resource allocation.
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