Quality evaluation of software engineering professional talent training under the background of new engineering

In order to address the issues of low recall rate, long clustering time, and low accuracy in the quality assessment of traditional software engineering talent cultivation methods, a new quality evaluation method of software engineering professional talent training under the background of new engineering is proposed. The intrinsic dependency relationship among the evaluation indicators of software engineering talent cultivation quality is analysed in depth using factor analysis, and a talent cultivation quality assessment indicator system is constructed. Indicator data is collected. The ant colony clustering algorithm is used to cluster the collected data, and the processed data is inputted into the talent cultivation quality assessment model based on fuzzy comprehensive evaluation to obtain relevant assessment results. The experimental results showed that the recall rate of this method is between 95% and 99%, the average clustering time of indicators is 7.75 s, and the maximum accuracy rate is 97%.