In the complex process industry, due to the lack of online detection instruments or harsh production environments, some key variables are difficult to measure and cannot be measured online. Therefore, research on soft sensing modeling of these variables is needed in process industries. Currently, deep learning based soft sensing modeling mostly focuses on feature modeling from a single perspective, neglecting valuable information from other perspectives, resulting in low accuracy of the prediction model. To address this issue, this paper proposes an industrial soft sensing modeling method based on a multi-view dual graph attention (Mv-DGAT) network. This method first constructs a multi-view framework, builds a spatial graph attention (SGAT) network based on the maximal information coefficient to complete the spatial perspective, and constructs a temporal graph attention (TGAT) network based on a multi-level temporal graph structure and the long short-term memory (LSTM)network to establish the temporal perspective. Secondly, the multi-head attention mechanism is used for spatiotemporal feature fusion prediction. Finally, the cosine similarity is introduced to evaluate the complementarity between views and suppress redundant features. The proposed method was tested on a publicly available dataset of real industrial processes. The experimental results showed that the proposed method has high prediction accuracy, with determination coefficients R2 reaching 0.85 and root mean square error reduced by more than 10% compared to the comparison model.