Abstract: A data-driven intelligent optimization method for fracturing treatment parameters was proposed to address the issues of insufficient pertinence and incomplete process design in digital fracturing treatment parameters. With 32 shale oil wells in the CD block as the research object, principal component analysis was used to reduce the 15 production-influencing factor dimensions representing geological attributes, engineering quality, and construction parameters of the reservoir. A Gaussian membership function and entropy weight method were introduced for a fuzzy comprehensive evaluation of reservoir fracturing heterogeneity. Combined with support vector regression and particle swarm optimization algorithms, the perforation location, segment length, cluster spacing, fracturing fluid intensity, sanding intensity, and discharge capacity were recommended with the highest production as the goal. The research results indicated that permeability, porosity, free hydrocarbon content by pyrolysis, fracturing fluid intensity, and sanding intensity were the main control factors for the production of the target block. All eight clusters of the first fracturing section of the application well have successfully initiated fractures during treatment with optimized parameters, with a half-length of 59.50–154.80 m and a production prediction accuracy of 94.86%. The method proposed can achieve effective reservoir quality evaluation, production prediction, and rapid optimization of treatment parameters that match reservoir geological conditions, promoting efficient shale oil development in unconventional reservoirs.