The detection of wear particle content in engine lubricating oil is crucial for preventing engine wear. Accurately and rapidly detecting the wear particle content in lubricating oil can timely assess the wear condition of mechanical equipment. To rapidly and efficiently detect the wear particle content in the solid-liquid two-phase flow formed by lubricating oil wear particles, a method combining near-infrared spectroscopy with mathematical modeling algorithms for predicting the wear particle content is proposed. Through the near-infrared absorption spectroscopy experimental system built, the spectral data of wear particle concentration in the range of 6～15 μg·mL-1 were collected by using a near-infrared spectrometer with a wavelength detection range of 900～2 500 nm under a total of 10 groups of working conditions under two kinds of metal wear particles, Fe and Cu, and five different particle sizes. To address the issue that spectral information at single-wavelength points cannot adequately explain the changes in wear particle concentration within the lubricating oil, the sample set partitioning based on joint x-y distances (SPXY) algorithm was employed to segment the spectral dataset. The partial least squares (PLS) model for predicting the wear particle content of lubricating oil was established, and the model prediction results under each working condition were analyzed. The results showed that wear particles could be effectively detected under each working condition, the highest coefficient of determination (R2) for the model was 0.831 8. Various data preprocessing methods were employed to correct the raw spectral data before modeling to address the issue of less-than-ideal prediction performance when using PLS modeling alone. The results showed that, except for a few abnormal conditions, the coefficient of determination R2 for the models under other conditions was greater than 0.8, optimizing the predictive performance of the PLS model. To further optimize the prediction effect of the lubricating oil wear particle model, the lubricating oil wear particle genetic programming (GP) model and the lubricating oil wear particle genetic program-partial least squares (GP-PLS) model were established, respectively. Compared with the PLS optimization model, the GP model for predicting lubricating oil wear particle content was more robust and had a better prediction effect, and the highest R2 reached 0.956 2. The mean fiducial error (MFE) was 14.73%. The GP-PLS model, compared to the GP model, achieved the highest R2 of 0.943 0 and a maximum MFE of 10.86%, improvement in MFE, thereby enhancing the predictive accuracy of the model. Through research and analysis of wear particle content prediction models, it has been concluded that various models can effectively predict changes in wear particle content in lubricating oil. Among them, the GP-PLS model performs better predicting wear particle content changes. The research results indicate that using spectroscopic analysis combined with model algorithms to predict wear particle content in the solid-liquid two-phase flow of lubricating oil is feasible, providing an effective detection method for detecting mechanical wear faults in engine equipment.