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Aiming at the difficulties in characterizing rolling bearing fault features with current diagnosis methods and the degradation of detection performance in a strong noisy environment, a method of rolling bearing fault diagnosis based on weighted dense connection network and attention mechanism is proposed. The method consists of feature extraction and fault classification. In the feature extraction part, firstly, the weighted dense connection network is used to extract features from the bearing vibration signal, and the features of different spatial levels are combined to enhance the diversity of information. Then, attention mechanism is used to highlight important information to obtain accurate fault features. The fault classification model takes the characteristic information as the input and outputs the diagnosis results of each fault type through softmax function. Experimental results show that the proposed model has good diagnostic performance in the case of additive noise interference, and has more advantages than other methods.