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The accurate measurement of soil moisture is of great significance to the fields of watersaving irrigation, moisture monitoring, water and fertilizer integration, and the soil nitrogen content will affect the measurement of the moisture sensor. In order to eliminate this effect, a monitoring experiment of different urea on soil samples with different moisture contents was designed, a highsensitivity moisture sensor was used to monitor the output voltage value under the interference of urea, and the moisture content of the soil sample was monitored by weighing method, the capacitance and resistance of the soil sample was monitored by LCR bridge tester. Totally 800 sets of sample data were obtained, of which 75% were used as the training set and 25% were used as the validation set. In order to study the mechanism of the influence of nitrogen content on moisture measurement, three predictive models, including a threeelement cubic polynomial model, a BP neural network model, and a deep learning model, were established based on experimental data, and error analysis was performed on the prediction results. The analysis showed that the highest errors of the three models were 2.86%, 2.07% and 3.82%, and the errors were concentrated in the range of 0 to 2%, accounting for 89%, 98% and 90%, respectively. The following conclusions were obtained: different urea contents had different influences on the predicted value, which was roughly in a periodic oscillation relationship. When the soil moisture content was lower, the interference of urea content on voltage was greater, and the same was true for impedance, but capacitive reactance was only sensitive to soil moisture, but not to changes in urea. Therefore, the interference of urea on soil moisture measurement was mainly caused by interference with soil resistance. The average absolute errors of the threedimensional cubic polynomial, BP neural network, and deep learning models were 0.77%, 0.64% and 0.75%, respectively, and BP neural network model was the most stable, with the most prediction results concentrated in the low error range. No matter how the urea content changed, the BP neural network prediction value curve can always track the actual mass moisture content curve very well. BP neural network model was superior to other network models in terms of prediction accuracy and stability. Therefore, BP neural network moisture prediction model was established as the best antiurea interference model. The interference of nitrogen content on moisture measurement was eliminated by increasing the data dimension and the prediction model was established, and the prediction accuracy was improved. 