Tree canopy cover (TCC) is a key biophysical indicator of forest structure and function, and long-term fine-resolution TCC data are essential for quantifying carbon cycling, monitoring forest succession, and supporting forest management under ongoing climate and land-use change. However, accurate long-term national-scale TCC mapping remains challenging because strong spatial heterogeneity requires large and representative reference samples that are difficult to obtain consistently across space and time. This challenge is particularly evident in China, where early historical TCC baselines and long-term annual TCC records remain insufficient. To address this gap, we developed an integrated framework that combines active learning, Random Forest, and stand-growth-based historical backtracking to reconstruct China TCC at 30 m (ChinaTCC30) for 1975, 1980, and annually from 1985 to 2024. Specifically, the historical TCC for 1975 and 1980 was reconstructed from MSS imagery using pseudo-samples generated under limited reference conditions, while annual wall-to-wall TCC maps for 1985–2024 were produced from Landsat imagery using visually interpreted TCC samples. Pixel-level uncertainty was further quantified using multi-model predictions. Validation against multiple independent reference datasets demonstrated the robustness and reliability of the resulting dataset, including visually interpreted samples (R² = 0.83, RMSE = 15.14 %) and multi-period National Forest Inventory data (6th<span> </span>–<span> </span>9th NFI; R² = 0.66<span> </span>–<span> </span>0.82, RMSE = 14.68 %<span> </span>–<span> </span>18.81 %). Compared with existing products, the resulting ChinaTCC30 dataset shows good national-scale spatial consistency while better capturing fine-scale spatial heterogeneity and long-term temporal continuity. As a spatially explicit TCC dataset spanning nearly five decades, it provides valuable support for forest monitoring, climate change research, and forest conservation and management, but also offers a cost-effective reference for TCC mapping at national and regional scales. The ChinaTCC30 dataset is available via an open-data repository: Part 1 (1975, 1980, and 1985–1988; DOI: <a href="https://doi.org/10.5281/zenodo.21194015">https://doi.org/10.5281/zenodo.21194015</a>), Part 2 (1989–1994; DOI: <a href="https://doi.org/10.5281/zenodo.21195911" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21195911</a>), Part 3 (1995–2000; DOI: <a href="https://doi.org/10.5281/zenodo.21199497" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21199497</a>), Part 4 (2001–2006; DOI: <a href="https://doi.org/10.5281/zenodo.21245669" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21245669</a>), Part 5 (2007–2012; DOI: <a href="https://doi.org/10.5281/zenodo.21262725" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21262725</a>), Part 6 (2013–2018; DOI: <a href="https://doi.org/10.5281/zenodo.21263677" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21263677</a>), and Part 7 (2019–2024; DOI: <a href="https://doi.org/10.5281/zenodo.21265832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21265832</a>) (Yu et al., 2026a, b, c, d, e, f, g).