In recent years, research on facial expression Recognition (FER) has become an increasingly active research topic. Deep learning is a new area, which gives a new way to classify images of human faces into emotion categories. However, it faces many difficulties caused by poor robustness and real-time performance. This paper designs a new architecture network based on Broad Learning System (BLS) for facial expressions Recognition. It is established as a flat network. The original inputs are transferred and placed as mapped features in feature nodes, while the structure is expanded in wide sense in the enhancement nodes. To evaluate our architecture we tested the proposed method with the Extended Cohn-Kanade Dataset (CK+). The experimental results show that the BLS approach is very effective in facial expression Recognition to compare with convolutional neural networks.