Due to the fact that traditional information fusion methods mostly rely on pre established theoretical mechanism models, the complexity of multi-source information in practice leads to certain deviations in the theoretical mechanism models, which affects the accuracy of information fusion. To address this issue, this study proposes a data-driven strategy based on the fusion of multi-source personalized news page information. Firstly, a high concurrency distributed crawler framework is used to obtain multi-source news pages online, and multi-source information is extracted from the obtained news pages. Then, based on the data-driven strategy, the multi-source information is fused to realize the personalized recommendation of multi-source news webpage information. The experimental results show that after applying this method, the root mean square error of the fusion result of the personalized news webpage information is only 0.17, which indicates that the method achieves more accurate fusion results.