The core mission of intelligence analysis is to extract event associations and perform causal inference from massive data. However， the existing Large Language Model （LLM）-based methods are constrained by the context window and computational complexity in processing long texts， so as to have difficulty in effective capture of causal associations between events， leading to a significant decline in inference capability， particularly in language models with limited parameter sizes. To address this issue， a Multi-Agent collaborative Knowledge Reasoning （MAKR） framework based on a dual tower structure was proposed. In the framework， the complex associations among entities were modeled explicitly through incremental construction of an entity relationship graph， thus assisting LLMs to achieve more accurate causal inference. The dual tower structure was designed to enable the graph model and the language model to process graph structure information and textual information， respectively. The comprehension ability of the model for complex logical relationships within long texts was enhanced through a fusion mechanism. Additionally， a prediction task for node relations in the graph was added on the basis of the language prediction task， thereby further optimizing the semantic alignment effect. Experimental results demonstrate that under conditions of limited computational resources， MAKR framework achieves superior performance in both event prediction and causal inference compared to the existing methods such as HetGNN （Heterogeneous Graph Neural Network） and HiGPT in the tasks of security event analysis of the GDELT dataset and sanction associated analysis of the OpenSanctions dataset. In conclusion， the practical value of the framework in industrial scenarios with constrained computational resources was validated.