With the rapid advancement of computational fluid dynamics （CFD） technology， the Reynolds-Averaged Navier-Stokes （RANS） model has gained widespread application in scientific research and engineering as a vital tool for turbulence simulation. For the large-scale， complex flow field data generated by RANS models， visualization techniques have become a key method for understanding and analyzing flow field characteristics due to their intuitive and visual nature. This paper systematically reviews the current state of information visualization in CFD flow field visualization， focusing on the analysis of various practical visualization methods. Traditional visualization techniques encompass scalar and vector field visualization， volumetric rendering， multiscale representation， as well as interactive and immersive visualization. Modern web technologies like WebGL offer new avenues for online visualization and cross-platform presentation of flow field data. Through analyzing and summarizing these visualization methods， this paper identifies current challenges in CFD flow visualization， including large-scale data processing， multivariable coupling， and vortex feature recognition. Discussions address these issues through CFD-driven machine learning， edge computing， and real-time interaction approaches. The paper concludes with a prospective outlook on CFD flow visualization development， providing theoretical insights for future visualization research.