The quantitative analysis of intravenous drug solute concentration has always been the research hotspot of drug detection in static dispensing centers. Still, the conventional means of mixing and reviewing are operated manually. There are problems such as limited control of the concentration of the drug solution, laborious pressure of manual review, and inefficiency, so it is crucial to propose an accurate, convenient, and non-destructive detection method for intravenous drug solute concentration. Due to the limitations of traditional near-infrared spectroscopy for the detection of low-concentration liquids, based on tunable laser absorption spectroscopy (TDLAS) technology, a quantitative detection model of glucose mixed solution concentration based on efficient attention mechanism one-dimensional convolutional neural network (ECA-1D-CNN) was investigated. In order to detect the low concentration of glucose mixed solution, based on the TDLAS technology, the 980 nm band with the highest light intensity absorption rate was selected as the laser light source, and through the photoelectric sensor, the transmitted light intensity signal of the drug concentration was acquired, which was demodulated into the second harmonic signal by the phase-locked amplification module to obtain a total of 600 self-constructed datasets of different concentrations, and the samples were divided into training and testing sets in the ratio of 8∶2. Aiming at the second harmonic signal of the transmitted light intensity of 600 drug concentrations as the research object, a glucose mixed solution concentration detection model based on the one-dimensional convolutional neural network model with efficient attention mechanism (ECA-1D-CNN) is proposed, with a total of four convolutional layers, all of which are activated by the Relu activation function, and a BN layer is added after each convolutional layer, a pooling layer is added after every two convolutional layers, and a pooling layer is added after the 2nd pooling layer, and a pooling layer is added after the 2nd pooling layer, and a pooling layer is added after the2nd pooling layer. Adding 1 ECA after the 2nd pooling layer can help the network model to learn the relationship between features better, reduce the number of parameters, and improve the robustness of the model. First, to highlight the advantages of the 1D-CNN model, the same original dataset is used to model PCR, SVR, and PLSR and compare the prediction effects of the 4 different models. Second, based on six different data preprocessing, the ECA-1D-CNN model was compared with the 1D-CNN model to analyze the generalization ability of the prediction model by using the coefficient of determination R², the absolute error MAE, and the root-mean-square error RMSE as the evaluation indexes. The results showed that the ECA-1D-CNN model under SG+Normalization preprocessing was the most effective, which was able to effectively predict the concentration of glucose mixed solution from 6 to 30 mg·100 mL^-1, and the R²of the model's training set could reach 0.998, the MAE 0.295, and the RMSE0.343, and the R² of the test set could reach 0.993, MAE of 0.498, RMSE of 0.691. The proposed method can accurately predict the concentration of intravenous drug solutes, which provides a new idea and an application value for the nondestructive testing of intelligent static dispensing centers.