BACKGROUND A standardized method for identifying medical laboratory observations, such as Logical Observation Identifiers Names and Codes (LOINC), is critical for creating accurate and effective public data models. However, such standards are not being used effectively. Standardized mapping facilitates consistency in medical terminologies and data sharing in multicenter treatment. OBJECTIVE To address the problem of standardizing laboratory test terminologies, a deep learning–based high-precision end-to-end terminology standardization matching system was developed to map laboratory test terms (LTTs) to LOINC. METHODS We manually constructed a laboratory test terminology mapping dataset containing 15,349 data items extracted from the information system of the Shengjing Hospital of China Medical University and matched 2,375 LOINC. We developed Attribute-wised Graph Attention Siamese Network (AGASN), a deep learning–based high-precision laboratory test terminology mapping model, to separately extract LTT features and LOINC term features and calculate the matching rate. We designed an attribute pooling mechanism to convert terminology strings to attribute sequences. Moreover, we developed a graph attention model based on attribute relations, which increased the interpretability of the proposed model. The problem of inconsistency in training and testing objectives was solved by improving the training objectives of the model. RESULTS The proposed a novel deep learning model achieved an accuracy of 82.33% ± 0.6% on the test dataset where the LOINC were visible, corresponding to a 10.9% improvement compared with that obtained using a random forest classifier. Furthermore, the proposed system achieved an accuracy of 63.14% ± 0.2% on the test dataset where the LOINC were invisible, constituting a 10.0% improvement compared with that obtained using SimCSE. Manual validation of the system performance showed accuracies of 82.33% and 70.66% on labeled and unlabeled datasets, respectively. Finally, we constructed a visual attribute relational strength network using an attribute graph attention model. CONCLUSIONS Herein, a Chinese laboratory test terminology mapping dataset was created and a deep learning system for the standardized mapping of LTTs was proposed. The results demonstrate that the proposed system can map LTTs to LOINC with high accuracy.