Multiple Sequence Alignment (MSA) is a core problem in computational biology, but traditional MSA tools struggle with high time and memory demands, particularly when handling large, dissimilar sequences. Existing methods, such as vertical segmentation, still face a trade-off between efficiency and accuracy. This study proposes FMAlign3, which addresses these challenges with two strategies. For large-scale similar sequences, we introduce a faster Star mode with parallel segmentation and a reconstructed core comparison algorithm, significantly boosting processing speed. For dissimilar sequences, we design the Pro mode with a horizontal segmentation strategy to improve accuracy while maintaining time efficiency. Experimental results show FMAlign3 achieves a 22-fold performance improvement over FMAlign2, with lower memory overhead and improved accuracy, particularly in dissimilar sequence alignment. Overall, the method improves performance by 20%. The program is available at: https://github.com/Aohy-github/FMAlign3 .