针对传统机器学习模型在识别PICO(population/problem, intervention, comparison and outcome)成分时存在特征提取不充分的问题，本文提出了一种自动识别医学文本中PICO成分的GRUCM模型，该模型融合了双向门控循环单元（bi-bated recurrent unit, BiGRU）神经网络和条件随机场（conditional random field, CRF）的优点，不仅能改善传统机器学习模型存在的特征抽取不足的问题，而且可以同时抽取出多个成分，避免创建多个模型而造成的资源浪费。该模型在测试数据上P成分的F1值为88.24%，I成分的F1值为80.49%，O成分的F1值为86.62%，与采用长短期记忆网络（long short-term memory, LSTM）和CRF模型的识别效果进行对比，本文提出的GRUCM模型对PICO成分的识别更有效。

The traditional learning model of machine learning has the problem of insufficient feature extraction when recognizing PICO components.A GRUCM model that automatically recognizes PICO elements in medical texts is proposed. This model combines the advantage of bidirectional gated recurrent unit ( BiGRU ) neural network and conditional random field(CRF). Not only the problem of insufficient feature extraction in traditional machine learning models can be improved, but also multiple elements can be extracted at the same time, avoiding the waste of resources caused by creating multiple models. Using the test dataset, the F1 of the P element is 88.24%, the F1 of the I element is 80.49% and the F1 of the O element is 86.62%. The recognition effect of long short-term memory(LSTM) neural network and CRF model is compared and analyzed, which shows that the proposed GRUCM model is more effective for PICO elements recognition.