For dealing with high-dimensional stationary time series, the factor model is often used to reduce the dimension. In this talk, we suggest a method of determining the number of factors in factor modeling. When the factors are of different degree of strength, the eigenvalue-based ratio method of Lam and Yao needs a two-step procedure to estimate the number of factors. As a modification of the method, however, our method only needs a one-step procedure for the determination of the number of factors. The resulted estimator is obtained simply by minimizing the ratio of the contribution of two adjacent eigenvalues. The finite sample performance of the method is well examined and compared with some competitors in the existing literature by Monte Carlo simulations and a real data analysis.