Aiming at the problems of cumbersome pre-processing and high false alarm rate of pipeline leakage signals under multiple working conditions,an ensemble empirical mode decomposition (EEMD) combined with improved convolutional neural network (ICNN) is proposed as a leakage identification model. The proposed identification method uses EEMD to decompose the leak signal into several intrinsic modal components (IMFs) with steady-state performance, and the noise dominant vectors are divided and removed by correlation coefficients to achieve signal reconstruction. A series of indicator features of the reconstructed signal are extracted as the input of the ICNN mode for feature extraction to achieve the multi-condition classification of pipeline. The batch normalization layer was added by ICNN between each convolutional layer and the pooling layer to accelerate the network training. In accordance with the results, it indicates that the proposed model can quickly and accurately identify pump shutdown, valve adjustment, leakage and normal operating conditions, which can reach 98.25% of the average recognition accuracy under less training data.This technique significantly raises the accuracy of recognition when compared to the unimproved CNN and SVM classification recognition models.