The invention discloses a wind turbine generator bearing fault diagnosing method based on a deep joint adaptation network. The wind turbine generator bearing fault diagnosing method based on the deepjoint adaptation network comprises the following steps: 1), establishing a multi-element fusion database; 2), establishing a deep joint adaptation model; 3), establishing a wind turbine generator bearing fault diagnosing model of the deep joint adaptation network; 4), establishing a multi-GPU cluster computing system. With the wind turbine generator bearing fault diagnosing method, according to distribution difference characteristics between training data and target data during monitoring under different actual working conditions, an inter-domain invariant characteristic representing and probability distribution difference correcting mechanism is explored, a fault target recognition strategy based on an inter-domain joint distribution adaptation and common characteristic deep learning fusion mechanism is provided, advantages of a deep self-encoding network can be utilized, the characteristics are not required to be artificially selected, better, abstract and advanced characteristics can be automatically extracted, and the computational complexity of a classification algorithm is reduced; the wind turbine generator bearing fault diagnosing method is especially suitable for a multi-noise, large-data, complex-structure and dynamic system.