Cold-chain transportation maintains the quality and safety of perishable food, thus reducing process losses. Monitoring the cold-chain environment is very important in ensuring its optimal performance. At present, the technology for monitoring the cold-chain environment enables multi-point, wireless, real-time measurements. Combined with new-generation intelligent information technology, there have been developments toward accurate predictions of the cold-chain environment. With a focus on predicting the cold-chain transportation temperature, this study proposes a method of time series temperature prediction based on a gated recurrent unit (GRU) artificial neural network that mines the time series information of historical data. First, the outliers and missing values of temperature data collected from refrigerated compartments are filtered out, then filled and corrected by Lagrange interpolation before being normalized. Comparisons of the prediction performance of the GRU models using time series data on three different scales lead to the construction of a GRU model for predicting the temperature changes of an experimental refrigerated compartment over the next 10 min. Finally, a recurrent neural network(RNN) and backpropagation neural network(BP) are used for comparative experiments. Comparing the actual temperatures with the predicted values, we find that the root mean square and average absolute errors of the compartment temperature predictions using the GRU model are 0.156°C and 0.760℃, respectively, and the average absolute percentage error is 0.236%. The GRU model exhibits better prediction performance than the recurrent neural network and backpropagation neural network. The research results are of significance for accurate early warning of food safety and precise control of the cold-chain environment.