Time-frequency analysis is a powerful tool to deal with non-stationary signals. As one of the traditional time-frequency analysis methods, s-transform can change the scale of its window function with frequency. However, the scale variation of time-frequency window function is fixed, which cannot be applied to the local characteristics of different signals, resulting in poor energy aggregation. In this paper, an adaptive generalized S-transform algorithm is proposed, and a generalized Gaussian window function controlled by four adjusting parameters is designed. The adjustment parameters are optimized by the adaptive concentration measurement to seek the best time-frequency characterization effect. According to the results of time-frequency analysis, instantaneous frequency recombination and component reconstruction are used to obtain the instantaneous frequency of each component, and at the same time, smooth processing is carried out to achieve the parameter estimation of multi-component signals. Simulation results show that the proposed adaptive generalized S-transform algorithm, combined with instantaneous frequency recombination and component reconstruction signal method, greatly improves the time-frequency resolution of multi-component signals and the accuracy of signal separation.