Multi-modal prediction tasks typically require the simultaneous modeling of heterogeneous data， including text， images and structured numerical information， to achieve robust inference and explainable decision-making in complex environments. Traditional uni-modal or weak fusion methods struggle to consistently address semantic alignment， information complementation and cross-source reasoning， while the inherent black-box nature of deep models limits the result interpretability. Meanwhile， the large language model（LLM） has demonstrated strong capabilities in semantic understanding， instruction following， and reasoning， yet a gap remains in their performance for time series modeling， cross-modal alignment， and real-time knowledge integration. To address these challenges， this paper proposes a LLM-guided multi-modal time series-semantic prediction framework. By combining variational inference-based time series modeling with LLM -driven semantic analysis， the approach establishes a collaborative “temporal-semantic-decision” mechanism： The temporal module extracts historical behavior patterns using recurrent latent variables and attention mechanisms； the semantic module distills high-level semantics and interpretations through domain-specific language models and multi-modal encoders； and both components are jointly optimized via a learnable fusion module， which also provides uncertainty annotations and explainable reports. Experiments on the StockNet， CMIN-US， and CMIN-CN datasets demonstrate that the approach achieves an accuracy of 63.54%， an improvement of 5.31 percentage points over the best baseline and an Matthews correlation coefficient （MCC） elevated to 0.223. This study offers a unified paradigm for multi-modal time series prediction and underscores its promising application in the field of financial technology.