Action Quality Assessment (AQA) has wide applications in various scenarios. Regarding the AQA of long-term figure skating, the big challenge lies in semantic context feature learning for Program Component Score (PCS) prediction and fine-grained technical subaction analysis for Technical Element Score (TES) prediction. In this paper, we propose a Localization-assisted Uncertainty Score Disentanglement Network (LUSD-Net) to deal with PCS and TES two predictions. In the LUSD-Net, we design an uncertainty score disentanglement solution, including score disentanglement and uncertainty regression, to decouple PCS-oriented and TES-oriented representations from skating sequences, ensuring learning differential representations for two types of score prediction. For long-term feature learning, a temporal interaction encoder is presented to build temporal context relation learning on PCS-oriented and TES-oriented features. To address subactions in TES prediction, a weakly-supervised temporal subaction localization is adopted to locate technical subactions in long sequences. For evaluation, we collect a large-scale Fine-grained Figure Skating dataset (FineFS) involving RGB videos and estimated skeleton sequences, providing rich annotations for multiple downstream action analysis tasks. The extensive experiments illustrate that our proposed LUSD-Net significantly improves the AQA performance, and the FineFS dataset provides a quantity data source for the AQA. The source code of LUSD-Net and the FineFS dataset is released at https://github.com/yanliji/FineFS-dataset.