Abstract: Objectives As China's offshore wind energy development advances into ultra-deep and remote offshore zones, severe maritime conditions (wave heights: 2.5–3.5 m) have emerged as critical operational and maintenance (O&M) constraints. Conventional wave compensation trestles exhibit limitations in prediction accuracy, response latency, and control strategy efficacy. These deficiencies compromise vessel-platform motion mitigation, elevate personnel transfer safety risks, and restrict the utilization of sea state windows. This research proposes an integrated solution employing a Fractal Reinforcement Learning (FRL) adaptive control algorithm combined with a six-degree-of-freedom (6-DOF) hydraulic trestle system, aiming to enhance O&M safety and efficiency. Methods Focusing on a deep-sea wind farm off Guangdong Province, the study leverages three years of sea state measurement data to develop the FRL-based adaptive compensation algorithm. This approach synthesizes wave fractal self-similarity with Deep Deterministic Policy Gradient (DDPG) continuous control. A 6-DOF hydraulic trestle implementing feedforward-feedback dual-mode control and multilayered safety protocols was engineered. Six months of extensive field trials, spanning typhoon and winter seasons, were conducted to evaluate system performance through ablation studies and statistical validation, complemented by a comprehensive economic analysis. Results Under challenging sea states (wave heights: 2.5–3.5 m; wind speeds: 15–25 m/s), the system sustained compensation errors below 0.5 m with response times ranging from 0.08 to 0.09 seconds. No incidents of personnel transfer accidents occurred. The sea state window utilization rate improved to 81.0% (a 9.1-fold increase), and personnel transfer throughput reached 31 individuals per day (a 3.4-fold uplift). The five-year static ROI was calculated at 338.1%, with a payback period of less than one year (0.98 years). Statistical T-tests on ablation results confirmed that both the fractal interpolation module and reinforcement learning components significantly enhanced compensation precision. Conclusions The framework facilitates comprehensive integration of algorithmic accuracy, system dependability, and economic viability, culminating in a cohesive "algorithm-system-verification-performance" architecture. It employs a closed-loop optimization mechanism leveraging fractal interpolation, reinforcement learning, and dual-mode control combining feedforward and feedback strategies. Validated through a six-month offshore typhoon season field trial and five years of quantitative assessment, it offers robust technical support for remote offshore and deep-sea wind power operations and maintenance. Additionally, it provides a standardized reference model for complex marine engineering applications, such as adaptive control of marine equipment, presenting a scalable design approach and validation methodology.