Dialogue State Tracking （DST） is an important module in task-oriented dialogue systems， but the existing open-vocabulary-based DST models do not make full use of the slot correlation information as well as the structural information of the dataset itself. To solve the above problems， a new DST model named SCEL-DST （SCE and LOW for Dialogue State Tracking） was proposed based on slot correlation information extraction. Firstly， a Slot Correlation Extractor （SCE） was constructed， and the attention mechanism was used to learn the correlation information between slots. Then the Learning Optimal sample Weights （LOW） strategy was applied in the training process to enhance the model's utilization of the dataset information without substantial increase in training time. Finally， the model details were optimized to build the complete SCEL-DST model. Experimental results show that SCE and LOW are critical to the performance improvement of SCEL-DST model， making SCEL-DST achieve higher joint goal accuracy on both experimental datasets. The SCEL-DST model has the joint goal accuracy improved by 1.6 percentage points on the MultiWOZ 2.3 （Wizard-of-OZ 2.3） dataset compared to TripPy （Triple coPy） under the same conditions， and by 2.0 percentage points on the WOZ 2.0 （Wizard-of-OZ 2.0） dataset compared to AG-DST （Amendable Generation for Dialogue State Tracking）.