Aiming at the problem of low recognition accuracy of human lower limb motion state， a lower limb motion state recognition prediction method based on plantar pressure is put forward. The plantar pressure data of 40 groups at different walking speeds collected by EMED plantar pressure plate are used as test samples. By analyzing the characteristic parameters of plantar pressure， the gait phase is constructed， and the gait period relationship and the gait displacement model are established. The lower limb movement is nonlinear， and the plantar pressure prediction is realized by gait cycle model combined with particle filter algorithm. Firstly， the prior probability density function is obtained by particle swarm initialization， and the predicted pressure is estimated. Secondly， the state vector is tested， and the predicted plantar pressure is deduced by multiple linear regression. Experimental results show that the particle filter algorithm has good performance at different gait speeds with the accuracy of more than 97%， and the plantar pressure prediction is effective. The plantar pressure data of participants of different ages， genders and weights are added for analysis and the prediction accuracy is all over 97.5%， verifying the stability and accuracy of the prediction algorithm.