Time series data are widely used in fields such as power load forecasting and meteorological analysis. Extracting high-quality representations of time series is crucial for downstream prediction tasks. However， the performance of the existing methods is limited by high-frequency noise interference， difficulty in modeling long-term dependencies， and the scarcity of labels. Therefore， a time series representation method based on Spectral Filtering and Hierarchical Dilation （SFHD） was proposed. Firstly， a Spectral Filtering Block （SFB） was designed to extract multi-scale features through global and local filters， and an adaptive spectral filtering mechanism was used in the frequency domain， so as to weaken the influence of high-frequency noise. Then， a Hierarchical Dilation Block （HDB） was constructed to use exponentially dilated convolutions to enlarge the receptive field progressively， thereby enhancing the ability to capture long-term dependencies. Finally， a change-aware self-supervised pretraining strategy was proposed to force the model to understand underlying structure of the series by masking the highly-dynamic data blocks， thereby alleviating the insufficiency of labeled data. Experimental results on seven public datasets with different prediction lengths demonstrate that， compared with the suboptimal model iTransformer （inverted Transformer）， the average Mean Square Error （MSE） of SFHD decreases by 9.47%， and the average Mean Absolute Error （MAE） decreases by 5.36%. It can be seen that SFHD provides stronger representation capabilities and leads to improved performance on downstream time series prediction tasks.