To further improve the convergence speed of the intelligent jamming decision-making algorithm based on value function in reinforcement learning and enhance its effectiveness, an improved Q-learning intelligent communication jamming decision algorithm was designed integrating the efficient upper confidence bound variance. Based on the framework of Q-learning algorithm, the proposed algorithm utilizes the value variance of effective jamming action to set the confidence interval. It can eliminate the jamming action with low confidence from the jamming action space, reduce the unnecessary exploration cost in the unknown environment, speed up its searching speed in the interference action space, and synchronously update the value of all actions, thus accelerating the optimal strategy learning process. The jamming decision-making scenario was modeled as the Markov decision process for simulation. Results show that when the correspondent used interference avoidance strategy against the jammer to change the communication channel, the proposed algorithm could achieve faster convergence speed, higher jamming success rate, and greater total jamming rewards, under the condition of no prior information, compared with the existing decision-making algorithms based on reinforcement learning. Besides, the algorithm could be applied to the “many-to-many” cooperative countermeasure environment. The action elimination method was used to reduce the dimension of joint jamming action, and the jamming success rate of the proposed algorithm was 50% higher than those of the traditional Q-learning decision algorithms under the same conditions.