The recovery factor of tight oil reservoirs is comprehensively controlled by formation geological properties, well spacing, and fracturing intensity. Insufficient formation drainage or fracture-hits between wells can lead to low recovery factors. The integrated optimization of well spacing and fracturing intensity is a crucial approach to improving recovery factors in tight oil reservoirs. This work analyzes the production characteristics of tight oil wells and identifies the main factors affecting productivity applying machine learning methods. An integrated workflow combining fracturing modeling, reservoir simulation, and production optimization is developed and applied to a real field reservoir block to explore the optimal matching relationship between well spacing and fracturing intensity. The research results indicate that the fractured horizontal well productivity is highly correlated with the stable water cut and is influenced by reservoir geological parameters and fracturing intensity. The material balance method can quickly reconstruct water saturation field after fracturing, which improves history matching. Moreover, simple machine learning method, response surface experiments, is used along with our integrated workflow to generate well spacing and fracturing intensity matching charts under different reservoir permeability conditions and provided the corresponding recovery factor prediction correlations. This study offers a fast and efficient method for developing tight oil reservoir with optimized well spacing and fracturing intensity.