Focused on the issues that the single deep distance metric based on triplet loss has poor adaptability to the diversified datasets and easily leads to overfitting， a deep distance metric learning method based on optimized triplet loss was proposed. Firstly， by thresholding the relative distance of triplet training samples mapped by neural network， and a piecewise linear function was used as the evaluation function of relative distance. Secondly， the evaluation function was added to the Boosting algorithm as a weak classifier to generate a strong classifier. Finally， an alternating optimization method was used to learn the parameters of the weak classifier and neural network. Through the evaluation of various deep distance metric learning methods in the image retrieval task， it can be seen that the Recall@1 of the proposed method is 4.2， 3.2 and 0.6 higher than that of the previous best score on CUB-200-2011， Cars-196 and SOP datasets respectively. Experimental results show that the proposed method outperforms the comparison methods， while avoiding overfitting to a certain extent.