In cognitive science, the bottleneck of information processing capabilities promotes humans to selectively focus on part of the visible information while ignoring the rest. This is usually called the attention mechanism. Among different attention mechanisms, spectrum attention focuses on assigning every frequency channel obtained by DCT an appropriate weight to achieve adaptive filtering and sends the reweighting frequency components into a real-valued neural network. However, DCT is a real-valued transformation for signal processing thus phase information tends to be omitted. Here we consider a departure from these conventions and propose a spectrum attention complex neural network assisted by DFT, to make a holistic frequency analysis in a completely complex domain. Our proposed network demonstrates outstanding performance on a one-dimensional hyperspectral dataset and MNIST dataset, compared to both traditional complex neural networks and DCT based frequency attention methods.