Modelling　and　predicting　the　suspect　activity　trajectory　are　of　great　importance　for　preventing　and　fighting　crime　in　the　food　safety　area.　Combing　artificial　intelligence　and　the　multiple　U-model　algorithm,　this　paper　represents　a　novel　approach　to　predict　the　suspect　activity　trajectory.　Based　on　social　text　data,　emotional　assessment　is　conducted　using　the　LSTM　network　to　detect　food　safety　criminal　suspects.　Activity　trajectories　of　criminal　suspects　are　clustered　using　the　graphic　clustering　method　based　on　the　GPS　data.　U-model　with　the　sliding　window　algorithm　is　proposed　to　model　activity　trajectories.　Further,　the　multiple　U-model　strategy　is　proposed　to　predict　the　activity　trajectory　based　on　the　accumulated　model　error　of　previous　positions　and　multiple　clustered　trajectories.　The　simulation　study　shows　that　the　proposed　scheme　can　detect　food　safety　criminal　suspects　and　predict　their　activity　trajectories　effectively.