Multivariate time series (MTS) forecasting has a wide range of applications in both industry and academia. Recent advances in spatial–temporal graph neural network (STGNN) have achieved great progress in modelling spatial–temporal correlations. Limited by computational complexity, most STGNNs for MTS forecasting focus primarily on short-term and local spatial–temporal dependencies. Although some recent methods attempt to incorporate univariate history into modeling, they still overlook crucial long-term spatial–temporal similarities and correlations across MTS, which are essential for accurate forecasting. To fill this gap, we propose a framework called the long-term multivariate history representation (LMHR) enhanced STGNN for MTS forecasting. Specifically, a long-term history encoder (LHEncoder) is adopted to effectively encode the long-term history into segment-level contextual representations and reduce point-level noise. A nonparametric hierarchical representation retriever (HRetriever) is designed to include the spatial information in the long-term spatial–temporal dependency modeling and pick out the most valuable representations with no additional training. A transformer-based aggregator (TAggregator) selectively fuses the sparsely retrieved contextual representations based on the ranking positional embedding efficiently. Experimental results demonstrate that LMHR outperforms typical STGNNs by 10.72% on the average prediction horizons and state-of-the-art methods by 4.12% on several real-world datasets. Additionally, it consistently improves prediction accuracy by 9.8% on the top 10% of rapidly changing patterns across the datasets.