MOTIVATION: Predicting the thermodynamic stability of proteins upon single-point mutations is a pivotal step in both protein engineering and medicine. In the study of predicting protein thermodynamic stability, various computational methods, whether they extract features at the local-level or global-level, exhibit their respective advantages and limitations. To leverage the advantages of both features, we developed MuFaDDG, a novel sequence-based method that integrated multiscale feature fusion for improved prediction of protein stability changes (ΔΔG). RESULTS: MuFaDDG achieves comparable performance on the S669 benchmark, demonstrating strong capabilities in stabilizing mutations. Notably, it shows a significant advantage in the ACC metric, with values of 0.75, 0.88, and 0.81 on the direct, reverse, and overall datasets of the CAGI5 Challenge's Frataxin, respectively. Furthermore, our method outperforms leading sequence-based approaches including THPLM, DDGemb, DDGun, and INPS-Seq on protein Myoglobin stability prediction. Additionally, MuFaDDG demonstrates exceptional predictive performance with higher PCC and ACC on the protein ThreeFoil, which is uncurated by FireProtDB and ProThermDB databases. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/PengjiaMa23/MuFaDDG.