Machine learning algorithms provide a research observation pathway for vocal technique training processes. This paper examines the impact of vocal technique training on singing ability and artistic expression, utilizing kernel ridge regression methods to study their specific enhancement relationships. Kernel methods are employed to transform relational issues into high-dimensional linear problems. Ridge regression algorithms are combined to enhance model fitting capabilities. Nonlinear regression methods (KRR) are integrated to improve regression performance in high-dimensional spaces. Research findings: The internal consistency reliability of the five influencing factors reached 0.936, with validity exceeding 0.7. The impact on singers’ artistic expression exceeded 0.06. The experimental group’s artistic expression was significantly superior to the control group at the 0.01 level, with average scores exceeding 90 points.
