Integrating ISA Optimised Random Forest Methods for Building Applications in Digital Accounting Talent Assessment

Abstract

Digital accounting talent assessment in applied undergraduate colleges and universities is an urgent problem of talent assessment construction. In order to solve the problem of digital accounting talent assessment in applied undergraduate colleges, a digital accounting talent assessment method based on improved machine learning algorithm is proposed. Firstly, the digital accounting talent assessment problem in applied undergraduate colleges is analysed, digital accounting talent assessment indicators are extracted, and the index system is constructed; secondly, the digital accounting talent assessment model based on the integrated ISA optimized random forest algorithm in applied undergraduate colleges is constructed by combining the integrated learning technology, the intelligent optimization algorithm, and the random forest; lastly, the digital accounting talent data in applied undergraduate colleges is used to analyse the model. The results show that compared with other algorithms, the accuracy of digital accounting talent assessment in applied undergraduate colleges and universities of Ada-ISA-RF is improved by 3.06 per cent and 7.04 per cent, respectively.