As medical informatization is constantly improving, electronic medical records have been more and more widely used, of which the unstructured text contains massive measurable quantitative information including patient clinical conditions. Due to the complexity of entities and quantitative information, it is a challenge to accurately extract measurable quantitative information. In this study, we propose the RPA-GRU model combining the relative position feature and attention mechanism based on a bi-directional gated recurrent unit. It incorporates the relative position feature into the attention mechanism to identify entities and quantity information. Meanwhile, the GATM model is proposed according to the reconstructed dependency tree-based graph attention network to learn graph-level representation, thus achieving the association between entities and quantity information. The experiment is based on 1359 electronic medical records from the burn injury department of a three-A hospital. The results show that the F1 values of RPA-GRU model and GATM model are 97.58% and 97.86% respectively in terms of identification and association of measurable quantitative information, up by 2.17% and 1.74% compared with the best-performing baseline model. In this way, the effectiveness of the proposed models is validated.