The non-damage, high speed, and ultra-high resolution characteristics of optical coherence tomography (OCT) technology in in vivo imaging application make it a broad space for development in the biomedical imaging field. Generally, a large amount of OCT data are acquired , and fast Fourier transform (FFT) in the image reconstruction process requires much calculation time. Therefore, the traditional central processing unit (CPU) serial data processing mode is difficult to meet the requirements of real-time imaging. To this end, the compute unified device architecture (CUDA) parallel programming technique is applied to the data processing of skin tissue imaging in the spectral-domain optical coherence tomography (SD-OCT) system, and it is implemented in the graphics processing unit (GPU). We detail the parallelization of the system algorithm and the parallel processing of the data collected by the system to improve the imaging speed. In the experiment, we use the SD-OCT system to image the skin of the finger and collect the data. The data collected by the laboratory's existing data processing platform MATLAB and GPU are processed respectively, and the imaging speed and image quality of different data processing platforms are compared. The experimental results show that the GPU and CPU hybrid programming model has a processing speed up to 10 times faster than the CPU-based MATLAB method while the image quality remains the same, which meets the clinical requirement of real-time imaging in the clinic.