Aiming at the problem that the model in the judicial field relation extraction task does not fully understand the context of sentence and has weak recognition ability of overlapping relations， based on Criminal-Efficiently learning an encoder that classi?es token replacements accurately （CriElectra）， an encoding-decoding relationship extraction model was proposed. Firstly， referred to the training method of Chinese Electra， CriElectra was trained on one million criminal dataset. Then， the word vectors of CriElectra were added to Bidirectional Long Short-Term Memory （BiLSTM） model for feature extraction of judicial texts. Finally， the vector clustering was performed to the features through Capsule Network （CapsNet）， so that the relationships between entities were extracted. Experimental results show that on the self-built relationship dataset of intentional injury crime， compared with the pre-trained language model based on Chinese Electra， CriElectra has retraining process on judicial texts to make the learned word vectors contain richer domain information， and the F1-score increased by 1.93 percentage points. Compared with the model based on pooling clustering， CapsNet can effectively prevent the loss of spatial information by vector operation and improve the recognition ability of overlapping relationships， which increases the F1-score by 3.53 percentage points.