链路预测对于揭示社交网络演化规律、节点间关系，以及理解复杂网络的典型机制具有核心意义。目前，针对融合时间演化、关系极性与边权重信息的复杂动态网络的链路预测研究仍显著不足，难以满足实际应用需求。本文面向动态符号加权网络，提出一种三重联合预测框架，以统一预测链路存在性、关系符号与边权重。首先，将动态网络分解为时序快照，并通过符号感知的加权随机游走生成节点语义嵌入；其次，设计多跳结构平衡特征与时序差异特征，分别刻画网络的结构性特征与动态演化规律；模型采用双通道特征解耦机制：节点语义嵌入用于链路存在性预测，而关系符号特征则输入Transformer编码器以建模时序依赖；最终，通过多任务单元协同输出预测结果。仿真实验表明，相较于基线方法，所提框架在链路存在性与关系符号预测性能上平均提升2%–4%，边权重预测误差显著降低40%–50%。

Link prediction is central to unraveling social network evolution and node relationships, as well as understanding the characteristic mechanisms of complex networks. Currently, research on link prediction for complex dynamic networks integrating temporal evolution, relational polarity and edge weight information remains significantly underexplored, failing to meet practical demands. For dynamic signed-weighted networks, this paper proposes a tripartite joint prediction framework for unified forecasting of links, signs and weights. First, the dynamic network is decomposed into temporal snapshots, and node semantic embeddings are generated via sign-aware weighted random walks. We then design multi-hop structural balance and temporal difference features to capture the structural characteristics and dynamic evolution laws of the network, respectively. The model adopts a dual-channel feature decoupling mechanism: node semantic embeddings are used for link existence prediction, while relational sign features are fed into a Transformer encoder to model temporal dependencies. Finally, prediction results are output synergistically through a multi-task unit. Simulation experiments demonstrate that, compared with baseline methods, the proposed framework achieves an average 2%-4% improvement in the performance of link existence and relational sign prediction, and a significant 40%-50% reduction in edge weight prediction error.