In　this　paper,　based　on　the　comprehensive　information　of　companies,　612　characteristic　parameters　are　extracted　and　mined,　and　two　prediction　models　of　the　categories　of　lawsuits　are　established.　The　first　model　is　the　combinatorial　prediction　model,　which　transforms　the　classification　problem　into　a　single-category　regression　problem.　After　the　Laplace　Smoothing　treatment　of　the　training　label,　LightGBM　model　was　used　for　the　5-fold　cross-validation　for　each　of　the　categories.　The　Top　1　and　Top　2　accuracy　of　the　final　combined　model　was　40.868%　and　21.826%,　respectively.　The　second　model　is　Artificial　Neural　Network　(ANN)　model,　which　directly　treats　the　problem　as　a　classification　problem.　The　ANN　model　with　five　layers　is　used　to　classify　and　predict　the　categories　of　lawsuits,　and　its　Top　1　accuracy　is　40.803%,　and　Top　2　accuracy　is　21.243%.　Although　the　accuracy　is　not　ideal,　but　the　method　is　feasible　and　can　be　used　for　reference.　Finally,　this　paper　analyzes　the　categories　of　misclassified　lawsuits　in　detail.