With the development of offshore wind power in China advancing to the deep sea, there are no buoys in the deep sea area, the wind speed near the coast is not large and unstable, and the flotation device is affected by complex factors including extreme environmental conditions (e.g., waves and ocean currents), so there exist problems such as the poor control effect of floating offshore wind turbines due to the low accuracy of wind speed measurement data. Under this background, an intelligent power control method for the converter in a deep-sea floating offshore wind turbine is proposed, which is based on a neural network effective wind speed estimator. The control system is composed of a proportional-integral-derivative (PID) controller, lookup table, a neural network estimator and a virtual sensor, in which the neural network estimator is trained online to adapt to the transformation of the environment, so as to estimate the effective wind speed of the floating offshore wind turbine and predict its future value. In addition, the current and future wind speed data are combined and further provided to the controller. The virtual sensor can also provide measurement of the effective wind speed based on other available factors (e.g., the pitch angle and angular velocity of a fan) related to the power generation of the fan. Finally, the effectiveness of the proposed control method is verified by simulation analysis. Compared with the traditional PID, this method can improve the accuracy of sinusoidal wind measurement by about 16%, and the average improvement rate is about 8%.