With the rapid development of meteorology, there requires a great need to better forecast dew point temperatures contributing to mild building surface and rational chemical control, while researches on time series forecasting barely catch the attention of meteorology. This paper would employ the seasonal-trend decomposition-based simplified dendritic neuron model (STLDNM*) to predict the dew point temperature. We utilize the seasonal-trend decomposition based on LOESS (STL) to extract three subseries from the original sequence, among which the residual part is considered as an input of an improved dendritic neuron model (DNM*). Then the back-propagation algorithm (BP) is used for training DNM* and the output is added to another two series disposed. Four datasets, which record dew points of four cities, along with eight algorithms are put into the experiments for comparison. Consequently, the combination of STL and simplified DNM achieves the best speed and accuracy.