Abstract
Accurate classification of budget items is a critical component of financial reimbursement, as it determines the legitimacy and regulatory compliance of financial expenditures. Currently, manual classification of reimbursement budget items faces to two challenges of inefficiency and inaccuracy. This is primarily due to the labor-intensive nature of the task, which increases the likelihood of selecting incorrect categories. To address these challenges, this study proposed a WeNet-Random Forest (WeNet-RF) model, which leverages speech recognition technology (WeNet) and Random Forest (RF) to improve efficiency and classification accuracy. WeNet-RF includes four steps: speech identification, features extraction, items classification, and evaluated model.This study compared WeNet-RF with Convolutional Neural Networks (CNN), Logistic Regression (LR) and K-Nearest Neighbors (KNN). WeNet-RF was verified by 50 real financial reimbursement records, and the results show that accuracy rate, precision rate, recall rate, and F1 score of WeNet-RF all are 90.77%. The findings provide a robust solution for improving financial management processes, and a reference model to financial management system.