Time series prediction involves learning existing observation data of a parameter and predicting its future evolution, with applications in many fields. Based on a machine/deep learning method, it is useful to predict the evolution trend of microseismic parameters during rockburst development. Our study explores key microseismic indices that help describe the development process of rockbursts based on abundant rockburst data obtained from deep underground engineering activities. On this basis, the structure of a deep convolution neural network is modified, and its input and output modules are improved, to realize a univariate, multivariate input and a single-step, multi-step output. Finally, various new models of the microseismic multi-parameters for time series prediction are proposed, including a univariate prediction model, a multiple parallel series model, a multiple input series model, and a multivariate multi-step prediction model. Model training, testing, and interpretation of the rockburst risk and a comparative analysis of the different models are carried out for the complete process of multiple rockburst disasters. The results show that the proposed models can predict the evolution trends of various key characteristics during a rockburst and ensure the timeliness and accuracy of rockburst risk prediction. They provide a new research idea for the prediction and early warning of rockburst disasters in deep underground and mining engineering.