In response to challenges in sea clutter modeling within the classical algorithms,including the lack of fitting accuracy due to the inability to satisfy multiple statistical characteristics simultaneously and the limitations in controllably generating accurate class-based results,combining the generative power of U-Net with the potential of complex-valued neural networks to deal with complex nonlinear problems in the electromagnetic domain, a novel approach is proposed. This approach integrates complex-valued network layers and a classifier-free guidance module, establishing an interpretable mapping mechanism for input conditions,resulting in complex-valued guided diffusion model(CVG-DM). This model is centered on the direct utilization of the complex-valued baseband signals from the In-phase and Quadrature(IQ) path of sea clutter, as well as the exploration of the relationship between sea clutter and strong targets in the background. This enables controlled generation of the model under varying conditions of target presence or absence, and assessment based on amplitude distribution, temporal and spatial correlation, nonlinear characteristics,and Doppler spectrum. Simulation experiment validates CVG-DM's capability in realizing sea clutter data augmentation under varying conditions. The simulated clutter can simultaneously take into account above five statistical properties, surpassing the completeness of real number network-based evaluation metrics and further enhancing fidelity.