<p>Accurate real-time estimation of the tropical cyclone (TC) intensity is crucial for TC forecasting and disaster management. Conventional intensity estimation methods like the Dvorak technique are subjective, and most deep learning (DL) approaches ignore the natural variability and time lag between the actual intensity and the remote sensing cloud pattern imagery. This paper introduces a multi-view sequence fusion network (MSFN), which is a novel DL framework that enhances TC intensity estimation by combining multi-channel cloud pattern imagery, sea surface temperature (SST), and statistical factors. The MSFN features three key innovations: a rotation-equivariant convolutional neural network (CNN) encoder to extract robust cloud pattern features, a transformer-based feature interactor to capture both temporal and cross-view dependencies, and a Gated Memory Fusion (GMF) module to dynamically integrate multi-view data and enhance robustness against noise. Tested on the full 2016–2019 TC data over the western North Pacific (WNP) basin, the MSFN achieves a coefficient of determination (<i>R</i>²) of 0.845 and a root mean square error (RMSE) of 4.6–5.2 m s⁻¹. When further evaluated on a strictly homogeneous subset of the same dataset, the MSFN achieves an <i>R</i>² of 0.848 and an RMSE of 4.3 m s⁻¹. Statistical analyses demonstrate that MSFN significantly outperforms the Advanced Dvorak Technique (ADT) and achieves comparable overall performance to the Satellite Consensus (SATCON) algorithm, with superior accuracy for weaker systems below 45 knots (= 23.1 m s⁻¹). Interpretability analyses and ablation studies confirm the model’s focus on meteorologically significant features and its own component synergy. The MSFN offers a reliable, interpretable tool for objective TC intensity estimation, advancing the TC forecasting capabilities.</p>