In　this　study,　the　multi-task　learning(MTL)　classification　method　based　on　CNN-BiGRU　model　is　proposed,　which　is　used　to　improve　the　accuracy　and　efficiently　of　legal　judgment　prediction.　The　subtasks　of　legal　judgment　prediction　are　law　artivles,　charges　and　the　terms　of　penalty.　However,　the　single　task　learning(STL)　models　are　used　to　analyze　legal　documents,　which　ignoring　the　correlation　among　the　subtasks.　The　MTL　model　of　CNN-BiGRU　enhance　the　task　learning　process,　which　can　extract　the　shared　information　among　subtasks　and　learn　multiple　tasks　at　the　same　time.　Therefore,　in　view　of　the　shorcomings　of　STL,　this　study　explored　the　affilication　of　the　MTL　method　to　predict　the　three　subtasks　of　legal　judgment.　CNN-BiGRU　has　combined　the　good　extraction　ability　of　CNN　for　local　feature　information　and　RNN　for　longterm　dependencies　information　of　the　text　classification.　Compared　with　the　CAIL2018-Small　dataset,　the　accuracy　and　F1-score　are　the　highest　of　all　baselines　models.　The　accuracy　and　F1-score　of　the　law　articles,　charges　and　the　terms　of　penalty　are　95.1%,95.2%,72.6%　and　95.2%,　95.4%,　72.7%,　respectively.　The　proposed　model　improves　the　interpretability　and　the　gneralization　ability.　The　effectiveness　and　suitability　of　the　model　are　validated　on　legal　judgment　prediction　tasks.　©　2020　IEEE.