A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel (AOK) was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%. A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel (AOK) was presented in this paper. First, the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed, and then the AOK theory was used to analyze the dynamic differ-ential pressure signal. The mechanism of two-phase flow was discussed through the time-frequency spectrum. On the condition of steady water flow rate, with the increasing of gas flow rate, the flow pattern changes from bubbly flow to slug flow, then to plug flow, meanwhile, the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band; moreover, when the flow pattern is slug flow, there are two wave peaks showed in the time-frequency spectrum. Finally, a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK. What are the characteristic variables were visu-ally analyzed, th e relationship between different combination of characteristic variables and flow patterns would be gotten. The results show that, this method can explain the law of flow in different flow patterns. And characteristic variables, defined by this method, can get a clear description of the flow information.This method provides a new way for the flow pattern identification, and the percentage of correct prediction is up to 91.11%.