Capturing complex spatiotemporal correlations in traffic data for forecasting remains a significant challenge. Most existing approaches ignore interactive learning between spatiotemporal features and extract them serially or in parallel. However, serial extraction leads to information coverage, while parallel extraction results in excessive parameters and challenges in model fitting. To address these issues, we propose the Interactive Multi-view Graph Convolutional Network. This model effectively learns spatiotemporal features interactively, capturing global dynamic spatial patterns in traffic data. Additionally, a fast parallel learning module is constructed within the multi-view learning module, enabling efficient local feature extraction with minimal parameters. A serial learning module further expands the receptive field. Extensive experiments on four real-world traffic flow datasets verify that the proposed model surpasses benchmark models in forecasting accuracy.