A method is proposed to evaluate, maintain and manage the long-term structural integrity and degradation behavior of offshore wind turbines: the multivariate or copula probability density function with an adaptive neuro-fuzzy inference system, which completes the assessment and management in three steps. First, the proposed method calculates the statistical population maximum likelihood estimation of the objective probability degradation characteristics and cycling patterns observed for historical precursors. Then, the method uses the Markov chain Monte Carlo method to generate hypothetical probability estimations of the objective features and synthesizes these probability density functions to build multivariate copula probability framework approximations of the features. The proposed method is applied to the integrity assessment and management of in-service offshore wind turbine support structures. Finally, the proposed method uses principal component analysis with a heat map to provide a helpful visualization for integrity assessment and integrity management tools. This method is expected to be embedded in existing supervisory control and data acquisition systems and to be used to construct a global integrity assessment model and management framework. This research shows the multivariate probability pattern of the integrity of an offshore structure under multielement coupling load conditions. A multivariate stochastic simulation is proposed for the goal-oriented integrity assessment, and an adaptive neuro-fuzzy inference system is used to manage the associated copula probability to quantify the coupling of risk because it uses comprehensive principal component analysis quantification models to describe the multivariate dynamic excitation behaviors of offshore wind turbine support structures. Meanwhile, multivariate hybrid probability frameworks are also shown to have high generalization ability and strong robustness. The proposed methodology also has great potential to be applied to other types of offshore systems for the characterization, assessment, and management of their integrity.