Extracting meaningful insights from temporal data through visualization plays a crucial in decision-making process. Conventional visualization methods such as stream graphs and stacked area charts suffer from clarity when applying to show trends of cross-categories over time. To alleviate this limitation, we propose the Sorted Stream Graph with Embedded Word Elements (SSGEW), an innovative approach that sorts stream segments and incorporates embedded word elements into a stream graph. Through an authored algorithmic development, our method enhances the arrangement of data categories and strategically places words to improve data exploration. We compare our visual design with two traditional techniques and demonstrate our approach through a case study on the evolution of automatically generated data visualization from 2017 to 2024. Our results show a clear distinction between SSGEW and the two other designs, especially when there are many fluctuations in cross-categories. Future work could be focusing on refining the design to overcome occlusion.