Low-light images often exhibit low brightness, small contrast, high noise, and color distortion, which significantly impair visual perception. To improve image quality, existing low-light image enhancement networks only consider single color-space feature extraction and overlook the powerful feature representation ability of wavelet transform. To this end, we propose a Color Space Feature Interaction and Dual-Domain Multi-Scale Collaboration Network (CS2DMNet) to enhance low-light image so that the quality of enhanced image can be in line with human visual perception. Firstly, considering that one image on the HSV and RGB color spaces has its intrinsic characteristics, interactive guidance between them is proposed to improve color recovery in the RGB space while mitigating noise amplification in the HSV space. Secondly, unlike previous methods using simple Haar wavelet transform, we introduce Dual-Tree Complex Wavelet Transform (DTCWT) into our method to achieve simultaneous spatial-domain and frequency-domain feature enhancement for brightness magnification, correct color distortion, and texture restoration. Thirdly, an Adaptive Threshold Adjustment (ATA) block is proposed to reduce noise in the high-frequency components from DTCWT decomposition. Extensive experiments on publicly available datasets show that CS2DMNet surpasses state-of-the-art methods, yielding excellent visual results in color recovery and dark detail enhancement.