A method for dynamic intelligent scheduling includes following steps: collecting and recording resource constraints of multiple schedules on a production line and decision data of changes made to the schedules by a scheduler; cross-enumerating schedule combinations by using multiple production goals as penalty conditions; establishing a mathematical model based on the resource constraints and multi-objective weights corresponding to each schedule combination and importing the resource constraints to calculate schedule results; recording the penalty condition corresponding to the schedule combination matching the decision data as a valid penalty; using values of parameters corresponding to the valid penalty and values of the penalty conditions respectively as inputs and outputs to train a learning model; and responding to a scheduling request, finding a weight of each schedule combination by using the learning model according to the resource constraint of the current schedule and the production goals, and generating a recommended schedule accordingly.