Abstract: To improve the accuracy about shale lithology identification, this paper proposes a ridgelet process neural network (RPNN) and selects the ridgelet transform as the active function of process neuron according to the signal characteristic of continuous and frequent variation about logging curve. RPNN uses the dynamic mechanism of AdaBoost to adjust iteratively weights of the models and the sample. In order to improve the RPNN learning speed in AdaBoost, an extreme learning algorithm based on full rank decomposition is proposed. The proposed method was applied to the lithology identification for B1 and B2 well in A area and its effectiveness was tested through comparison and analysis. The results show that the lithology identification result of RPNN is better than other process neural networks and the recognition accuracy of RPNN is up to about 90 per cent.