Facial expression recognition plays an increasingly important role in many fields. The richness of facial expressions and the ambiguity of expression recognition increase the difficulty of facial expression recognition. The recognition rate of existing algorithms is either high or complex. Either the complexity is low or the recognition rate is also low. Can a compromise be made between complexity and recognition rate? So, we designed a basic and effective convolutional neural networks (BASE) algorithm. In order to further improve the performance of the BASE algorithm, we propose a facial expression recognition algorithm based on efficient channel attention for deep convolutional neural networks (CECA-NET). We tested it on four commonly used facial expression databases. The efficient channel attention module increases the interactivity between channels and the key information that is not easy to be paid attention to is excavated. Experiments show that our method can obtain richer facial expression information and has stronger generalization ability.