The deep learning-based (DL-based) channel state information (CSI) feedback in the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system has demonstrated its potential and efficiency. However, conventional neural networks cannot fully utilize the complex-valued nature of the downlink channel. In addition, multi-scale and multi-resolution features of CSI can be further explored. In this letter, we present a complex-valued lightweight neural network for CSI feedback named CVLNet. The CVLNet adopts the complex-valued neural network components in a multi-scale feature augmentation encoder and a multi-resolution X-shaped reconstruction decoder with a series of lightweight details. The experiment results show that the proposed CVLNet maintains the same-level parameters of the encoder with state-of-the-art (SOTA) lightweight networks while outperforming them with at most a 33.4% improvement in accuracy under severe compression rates.