The present invention discloses a series-wound long short-term memory recurrent neural network-based heating load prediction method. The method comprises the steps of constructing a sample data set based on temperature, climate and heat supply data during a given period of time, and respectively subjecting the input data and the output data of the sample data set to standardized treatment; dividing the input data into two portions, respectively inputting the two portions into two independent long short-term memory recurrent neural networks to merge the two portions of the input data, inputting the output data to a long short-term memory recurrent neural network at a next layer, and finally inputting the data into two full connection layers; training a constructed series-wound long short-term memory recurrent neural network, and optimizing the network by adopting the parameter optimization-based adaptive torque estimation algorithm; inputting to-be-predicted data into the series-wound long short-term memory recurrent neural network, calculating and obtaining a heating load prediction result. The method of the invention can effectively discriminate input data, and accelerate the learning speed. Therefore, the learning efficiency is improved and the prediction accuracy is improved.