Aiming at the deviation of the recognition accuracy for pipeline system blocking recognition model under complicated working conditions, a method is proposed for recognizing blockage and lateral connection in pipeline individually based on timefrequency image and convolution neural network algorithm. Firstly, acoustic wave detection method is used to obtain lowfrequency sound pressure signals under different working conditions, smooth pseudo WignerVille timefrequency analysis method is performed to the filtered signal to obtain the timefrequency distribution map. Then, the Otsu threshold segmentation method is applied to adaptively segment the timefrequency distribution images to obtain timefrequency images of blockages and lateral connection under single and complicated working conditions. At last, the timefrequency images of light blockage, heavy blockage, lateral connection and pipe end under a single working condition are entered into the CNNSVM model for training, the trained parameter model is applied to the automatic recognition of blockages and pipe components under complicated working conditions. The experimental results show that the recognition rate of the proposed method for four kinds of targets under complicated conditions is over 96%, and the recognition accuracy is higher than that of the traditional artificial feature extraction model, which verified that the influence of the blockage on the acoustic wave under different working conditions is common and different from that of the lateral connection. individual analysis of different degrees of blockage and lateral connection under complicated working conditions individually, can effectively avoid the deviation of model recognition accuracy owing to the difference of working condition distribution.