With the rapid development of demand response technology, the load data of power system has been presented with large scale and complex nonlinear characteristics. And the load forecasting method based on deep learning and efficient data processing platform becomes the current research focus. Based on the Spark processing platform and the clock recurrent neural network(CW-RNNs), the short-term load forecasting method is established to realize the prediction of the ultra-short term load of the integrated energy systems with the demand response. Firstly, different working groups are set up on the Spark platform to divide all the data into multiple sub-data modules. The data processing efficiency is improved by the parallelization calculation, and the load curve is adjusted based on the dmand response to calculate the expected benefits and user comfort index value. Secondly, the discrete wavelet transform in used to decompose the adjusted load curve to obtain a set of high and low frequency signals. The low and high frequency signals are trained by the partial least squares regression model and CW-RNNs regression model respectively. Finally, the well-trained PLS model and the CW-RNNs model are combined to obtain the final combination forecasting model(Spark-CW-RNNs). The results show that the forecasting errors of Spark-CW-RNNs model is smaller than that of other single models, and the prediction accuracy is higher. The proposed ultra-short term load forecasting model is effective and feasible.