Time-to-event data are ubiquitous in clinical medicine research domain, and include a large number of time-dependent time-dependent risk factor variables. To effectively analyze the time-dependent time-to-event data and to overcome the limitation of parameter hypothesis of the survival model, a multi-task Logistic survival leaning and prediction method was proposed. The survival prediction was transformed into a series of multi-task binary survival classification problems at various time points, and all observations of time-dependent risk factors were used to estimate the cumulative risk. By learning all data of event samples and censored samples, the Logistic regression parameters were regularized. The time-dependent relationships between risk factors and time-to-event were evaluated, and the time-to-event was estimated according to the survival probability. The comparative experiments on multiple real clinical datasets demonstrate the applicability of the proposed multi-task prediction method for time-dependent data and that the method can guarantee the accuracy and reliability of the prediction results.