Significant Wave Heights (SWH) is an important attribute to describe ocean waves, and SWH prediction is of great significance for ensuring the design of offshore engineering and the safety of offshore operations. In recent years, deep learning methods have been used to predict SWH, but the existing methods cannot effectively capture the long-term correlation of SWH, thus ignoring the local associations between multiple elements of the ocean. To this end, this paper proposes a SWH prediction model (Multi-elements Local and Global Correlation for Wave height Prediction, MLG-SWH) that combines local and global features of marine multi-elements. First, using multiple factors such as significant wave height, wind speed and period as input, a Local-Global Embedding (LGE) module is designed to embed local correlation and time information of ocean multi-elements. Then, an encoder-decoder structure is used to extract the features of ocean wave height, where a casual dilated convolution self-attention module is designed to effectively capture the global long-term correlation of ocean multi-element sequences and the generative prediction method in the decoder is adopted to avoid errors accumulated in the single-step iterative prediction. Finally, the data of two stations with different characteristics of SWH variation in the North Atlantic are selected for experimental evaluations. Compared with classical time-series forecasting models and mainstream deep learning methods, the MLG-SWH model achieves the lowest mean square error and mean absolute error in 24 and 48 hours SWH forecasting, having a greater advantage in long-term time series prediction.