The optimization of hydraulic fracturing parameters is identified as the key to enhancing unconventional oil and gas development. To address current issues, such as the convergence of oilfield fracturing design and the challenge of fully reflecting reservoir physical property differences in fracturing design, the Mahu conglomerate reservoir was taken as the research object. The extreme gradient boosting(XGboost) algorithm was applied to conduct an in-depth evaluation of the fracturing and modification effect of the target reservoir. By combining this with thermodynamic plates and correlation analysis of characteristic parameters, the main controlling factors of the fracturing effect for different oil wells were identified. Subsequently, a simulated annealing parameter optimization algorithm was employed to derive the parameter combinations that can maximize production. Finally, a combined neural network model based on long short-term memory(LSTM), convolutional neural networks(CNN), and Kolmogorov-Arnold networks(KAN) algorithms was utilized to predict the output of a single well with high accuracy, achieving a prediction accuracy of 94%. This provides strong technical support for the field, as well as theoretical support and practical reference for the development of complex oil and gas reservoirs.