Waves are crucial in ocean observation and research. Stereo vision-based wave measurement, offering non-contact, low-cost, and intelligent processing, is an emerging method. However, improving accuracy remains a challenge due to wave complexity. This paper presents a novel approach to measure wave height, period, and direction by combining deep learning-based stereo matching with feature matching techniques. To improve the discontinuity and low accuracy in disparity maps from traditional wave image matching algorithms, this paper proposes the use of a high-precision stereo matching method based on Pyramid Stereo Matching Network (PSM-Net).A 3D reconstruction method integrating Scale-Invariant Feature Transform (SIFT) with stereo matching was also introduced to overcome the limitations of template matching and interleaved spectrum methods, which only provide 2D data and fail to capture the full 3D motion of waves. This approach enables accurate wave direction measurement. Additionally, a six-degree-of-freedom platform was proposed to simulate waves, addressing the high costs and attenuation issues of traditional wave tank simulations. Experimental results show the prototype system achieves a wave height accuracy within 5%, period accuracy within 4%, and direction accuracy of ±2°, proving the method's effectiveness and offering a new approach to stereo vision-based wave measurement.