The atmospheric humidity profile is a vital factor in studying the complexity of the atmospheric system. The groundbased microwave radiometer (MWR) can continuously observe and retrieve atmospheric humidity profiles up to 10 km with high temporal resolution. These profiles are essential for understanding the changes in the climate system. In order to improve the accuracy of retrieving the atmospheric humidity profile by MWR, this paper uses a time loop neural network model that uses the continuous detected signals of microwave radiometers. Moreover, Ka-band millimetre-wave cloud radar data is employed to improve the inversion accuracy for cloudy data. LSTM neural network is applied as the inversion method to retrieve atmospheric humidity profile, while radiosonde measures relative humidity as truth-value to verify and analyze the inversion effect. This research has also conducted a detailed comparison with classical inversion methods (BP and support vector machine). The average absolute error of the humidity profile and the sounding profile is 9.80%, the root mean square error is 13.85%, and the BP neural network model’s average absolute error is 11.52%. The root mean square error is 15.66%. The comparison proves that the method using temporal information could effectively improve the inversion accuracy, especially for the inversion of relative humidity in the range of 3 to 7 km, where the atmospheric humidity profile distribution is more complicated.