Target　parameter　estimation　in　high-speed　scenarios　is　one　of　the　main　challenges　in　the　integrated　sensing　and　communication　(ISAC)　systems.　In　an　ISAC　system,　the　orthogonal　time　frequency　space　(OTFS)　signal　is　able　to　successfully　combat　time-frequency-selective　channels　since　the　channel　exhibits　significant　delay-Doppler　(DD)　sparsity　characteristic.　In　this　paper,　we　investigate　the　problem　of　parameter　estimation　of　moving　targets　using　OTFS　modulation.　We　firstly　derive　signal　model　in　the　DD　domain　equivalent　channel　and　recast　the　problem　of　parameter　estimation　into　a　compressed　sensing　(CS)　problem.　In　order　to　improve　the　estimation　performance,　we　then　propose　ADMM-Net　by　deep　unfolding　the　iterations　of　the　Alternating　Direction　Method　of　Multipliers　(ADMM)　algorithm　into　a　deep　learning　network.　Experimental　results　demonstrate　that　the　proposed　ADMM-Net　algorithm　outperforms　the　other　methods　in　terms　of　estimation　accuracy　and　running　time　for　OTFS-based　parameter　estimation.