Gas–liquid two-phase flow widely exits in production and transportation of petroleum industry. Characterizing gas–liquid flow and measuring flow parameters represent challenges of great importance, which contribute to the recognition of flow regime and the optimal design of industrial equipment. In this paper, we propose a novel complex network-based deep learning method for characterizing gas–liquid flow. Firstly, we map the multichannel measurements to multiple limited penetrable visibility graphs (LPVGs) and obtain their degree sequences as the graph representation. Based on the degree distribution, we analyze the complicated flow behavior under different flow structures. Then, we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction. We implement the model with two parallel branches with the same structure, each corresponding to one input. Each branch consists of a channel-projection convolutional part, a spatial–temporal convolutional part, a dense block and an attention module. The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement. At last, our method achieves an accuracy of 95.3% for the classification of flow structures, and a mean squared error of 0.0038 and a mean absolute percent error of 6.3% for the measurement of gas void fraction. Our method provides a promising solution for characterizing gas–liquid flow and measuring flow parameters.

气液两相流在石油工业的生产和运输中广泛存在。表征气液流量和测量流量参数是非常重要的挑战，它们有助于识别流态和工业设备的优化设计。在本文中，我们提出了一种新颖的基于复杂网络的深度学习方法来表征气液流动。首先，我们将多通道测量值映射到多个有限的可穿透可见性图（LPVG），并获得它们的度序列作为图表示。基于程度分布，我们分析了不同流动结构下的复杂流动行为。然后，我们设计了一种双输入卷积神经网络，以融合原始信号和LPVG的图形表示，以对流动结构进行分类并测量气体空隙率。我们用两个具有相同结构的并行分支来实现模型，每个分支对应一个输入。每个分支包括一个通道投影卷积部分，一个时空卷积部分，一个密集块和一个关注模块。将两个分支的输出连接起来，并馈入几个完整连接的层中，以进行分类和测量。最后，我们的方法在对流动结构进行分类时达到了95.3％的精度，在测量气隙率时平均平方误差为0.0038，平均绝对百分比误差为6.3％。