With the integration of large-scale renewable energy and the increased electrification of power systems, issues related to the weakening of power system stability due to insufficient inertia levels have become frequent in recent years. Consequently, the inertia levels evaluating of high-renewable-energy power systems is crucial for developing effective inertia enhancement strategies and ensuring the safe and stable operation of the power system. A method for power system inertia evaluating based on a recursive least squares algorithm with variable forgetting factor is proposed. Firstly, a controlled autoregressive moving average (CARMA) model, incorporating Gaussian white noise, is developed to evaluate the inertia of the power system. The Akaike Information criterion (AIC) is used to determine the appropriate model order, addressing the issue of model overfitting. Then, an improved recursive least squares algorithm with an exponentially decaying variable forgetting factor is proposed to enhance the algorithm's ability to track dynamic changes in the measured data, thereby resolving data saturation issues and improving the accuracy of inertia evaluations. Finally, the effectiveness and superiority of the proposed method are verified through the case study.