Establishing a correlation between the friction coefficient and friction noise of metal-friction polymer interfaces is a challenging task in various environments. To address this issue, our study utilizes machine learning algorithms to construct a friction data-based model, elucidating the relationship between noise and friction coefficient. We propose the variational mode decomposition (VMD) along with five machine learning algorithms, each capturing unique data characteristics. Algorithm optimization is achieved through the implementation of L1 and L2 regularization methods. By comparing the regression results, we develop a tribological model that offers a novel approach for constructing a nonlinear model, enabling precise monitoring of the friction coefficient by leveraging noise signals.