The network security monitoring systems deployed on network nodes generate a large number of network-side alerts every day, causing the security engineers to face significant pressure to lose sensitivity to high-risk alerts and fail to detect network attacks in time. Due to the complexity and variability of cyber attacks and the limitation of network-side alert information, existing alert prioritization/ classification methods for IT operations are unsuitable for network-side alerts. Thus, network-side alert prioritization (NAP), the first network-side alert prioritization method, is proposed based on multivariate data fusion. NAP first designs a multi-strategy context encoder based on source IP address and destination IP address to capture the context information of network-side alerts. And then, NAP designs a text encoder based on the attention-based bidirectional GRU model and the ChineseBERT model to learn the semantic information of network-side alerts from the text data such as alert messages. Finally, NAP builds a ranking model to obtain the alert ranking values and then ranks the high-risk alerts with cyber attack intention in the front according to their descending order to optimize the network-side alert management process. The experiments on three groups of network attack and defense data from NSFOCUS show that NAP can achieve effective and stable prioritization results, and significantly outperforms the compared methods. For example, the average NDCG@k (kÎ[1,10]) (i.e., normalized discounted cumulative gain of the first 1 to 10 ranking results) ranges from 0.893 1 to 0.958 3, and outperforms the state-of-the-art method more than 64.73%. Besides, NAP has been applied to a real-world network-side alert dataset from Tianjin University, further confirming its practicability.