To solve the problem of low identification accuracy of two-phase flow pattern, a flow pattern identification algorithm based on two-channel hybrid network fusion support vector machine was proposed. Multi-scale feature extraction of rich feature layer information was carried out by multi-scale convolution check of capacitance vector. Squeeze-and-excitation networks was used to focus on the important feature tensor of convolutional kernel channel and adjust the importance proportion of each channel. In addition, multi-scale attention mechanism was introduced to learn the depth feature of capacitance vector. The multi-scale convolutional channel with SENet and multi-attention channel were fused to form a two-channel identification model. Finally, the features of capacitance vector effectively captured by the two-channel model were sent to support vector machine for training and testing. Simulation results show that compared with BP neural network, SVM and 1DCNN algorithms, the average identification rate of the proposed algorithm in flow pattern identification is significantly improved, reaching 98.6%.