In　massive　grant-free　non-orthogonal　multiple　access　(GF-NOMA)　systems,　multi-user　detection　usually　relies　on　the　prior　sparsity　of　signals　to　detect　active　users.　However,　in　practical　applications,　especially　in　dynamic　multiuser　access,　the　user　access　process　becomes　more　complex　and　obtaining　such　prior　information　becomes　more　difficult.　Therefore,　this　paper　proposes　a　learnable　threshold　optimization　scheme　for　massive　dynamic　multi-user　access　detection,　namely　the　threshold-improved　adaptive　alternating　direction　method　of　multipliers　(TI-A-ADMM)　algorithm.　In　this　algo⁃　rithm,　the　time　correlation　of　active　user　communication　is　utilized　to　introduce　a　dynamic　correlation　measure,　which　adap⁃　tively　scales　the　noise　threshold　for　active　user　detection,　thereby　improving　detection　performances.　Moreover,　to　enhance　the　accuracy　of　active　user　detection　across　different　signal-to-noise　ratios,　a　deep　learning　network　is　employed　to　optimize　the　initial　detection　threshold,　adapting　to　various　access　environments.　Simulation　results　indicate　that,　in　the　case　of　dy⁃　namic　multi-user　access　without　known　prior　sparsity　information,　the　proposed　TI-A-ADMM　algorithm　achieves　a　perfor⁃　mance　gain　of　2.4　dB　in　terms　of　active　error　rate　(AER)　and　symbol　error　rate　(SER)　compared　to　existing　algorithms　with　known　sparsity　information.　The　proposed　algorithm　exhibits　lower　performance　degradation　and　higher　robustness　against　interference　caused　by　multi-user　access.　©　(2025)　(Chinese　Institute　of　Electronics)　All　rights　reserved.