Change detection (CD) aims to explore surface changes in co-aligned image pairs. However, many existing networks primarily focus on learning deep features, without considering the impact of attention and fusion strategies on detection performance. Therefore, a new Frequency-Temporal-Aware Network (FTA-Net) is proposed, it recognizes changes by means of a frequency-domain temporal fusion module and supervised attention to multilevel time-difference features, while reducing the model size. Frequency Temporal Fusion Module is designed to introduce the frequency attention mechanism into the fusion process. First, it has a two-branch Transformer-INN feature extractor using a Lite-Transformer that utilizes remote attention for low-frequency global features, and a Invertible Neural Network that focuses on extracting high-frequency local information. The semantic information and details of the object in both highfrequency and low-frequency feature maps are further strengthened by fusing the high-frequency local features and low-frequency global representations. Then, a Stepwise Modification Detection Module is proposed to better extract temporal difference information from bitemporal features. In addition, a Supervised Learning Module is constructed to re-weight features to efficiently aggregate multi-level features from highlevel to low-level. FTA-Net outperforms state-of-theart methods on three challenging CD datasets, and it have fewer parameters (4.93 M) and lower computational cost (6.71 G). Our code is available at https://github.com/Ztjdsb/FTA-Net