The　multiple-input　multiple-output　orthogonal　frequency　division　multiplexing　(MIMO-OFDM)　technology　shows　great　potential　in　integrated　sensing　and　communication　(ISAC)　systems.　To　address　the　challenges　posed　by　the　high　computational　complexity　and　low　estimation　accuracy　of　traditional　target　parameter　estimation　methods　in　a　MIMO-OFDM　based　ISAC　system,　this　paper　introduces　a　compressed　sensing　based　approach　for　target　parameter　estimation.　We　first　derive　the　channel　model　within　the　MIMO-OFDM　system,　and　then　reformulate　the　problem　of　target　parameter　estimation　as　a　least　absolute　shrinkage　and　selection　operator　(LASSO)　problem.　Furthermore,　we　propose　a　model-driven　network,　termed　FISTA-Net,　which　deeply　unfolds　the　fast　iterative　shrinkage　thresholding　algorithm　(FISTA)　into　a　deep　neural　network　to　solve　the　LASSO　problem.　Experimental　results　show　that　the　proposed　FISTA-Net　algorithm　outperforms　the　existing　methods　in　terms　of　estimation　accuracy　and　computation　efficiency　for　MIMO-OFDM　based　target　parameter　estimation.　©　2023　IEEE.