Ultra-short-term logistics demand forecasting is important for intelligent scheduling of enterprise logistics resources. As ultra-short-term logistics demand data is random, highly volatile, and nonstationary, it is difficult to accurately predict them for multi-steps. Considering such characteristics, this research proposes a combination model for ultra-short-term logistics demand forecasting based on the serial data decomposition and quantum-weighted deep nerual network. Firstly, the time series features of the ultra-short-term logistics demand data are extracted with the decomposition method of serializing variational mode decomposition(VMD) and empirical wavelet transform(EWT) to strip the noise signal and reduce the non-stationarity and randomness of the original data. Secondly, a quantum- weighted long short-term memory neural network(QWLSTM) deep learning model is developed and a multi-input multi-output strategy is designed to predict the decomposed mode components in multi-steps, also the hyper-parameters of the QWLSTM are optimized by the Tree-Parzen-Estimator(TPE). Finally, the prediction results for each mode component are reconstructed. Numerous experiments are conducted, and the results show that the proposed model performs better than other 15 comparison models.