At present, tight shale oil requires fracturing technology to achieve production capacity, which can cause reservoir damage while transforming the reservoir. Analyzing the physical properties of tight oil reservoirs and the impact of fracturing fluid damage is the focus of this study, with a specific emphasis on shale oil extraction from the Chang 7 section of the Ordos Basin. Through a comprehensive approach involving experimental techniques such as reservoir property analysis, thin section casting, electron microscopy scanning, X-ray diffraction, and sensitivity testing, the intricate relationship between these physical properties and fracturing fluid-induced damage is examined. The use of traditional damage assessment methods requires a large amount of rock cores, which are increasingly valued as non renewable and precious resources. Therefore, adopting predictive methods is beneficial for protecting rock core resources, reducing experimental costs, and improving work efficiency. Hence, a mathematical prediction model is introduced, featuring an enhanced particle swarm optimization BP neural network with random search capabilities to expedite the convergence of the neural network. This model aims to delineate a sophisticated nonlinear correlation between the physical characteristics of the Chang 7 reservoir in the Ordos Basin and the consequential impact of fracturing fluid damage. Twenty-three data sets from the Chang 7 reservoir are employed for training purposes, with the remaining two sets reserved for validation. Both the predicted and actual experimental errors are under 10%. The analysis reveals that errors within and outside the prediction samples are minimal, highlighting the model's high prediction accuracy and strong generalization abilities. This model provides a solid theoretical basis for optimizing fracturing fluids.