Decision?making with multiple choices plays a key role on cognition. In this work,we extended a network model of Frontal eye field to a learning based model,and trained it to complete a cognitive task: non?choice taskthen used the simulation results to explain the cognitive process of multiplechoice decision?making. After thousands of trainings,the network model changed from selecting target randomly into choosing the largest reward-relative decision. In the training process,the sequence of multiplechoice decision was related to the reward gradient. In addition, the reward differences between distinct decisions played an important role on the learning speed of the network model,making the model exhibit two learning phases: the fast learning phase and the slow learning phase.