[Purpose/significance] Intelligence analysis in the era of big data faces many challenges such as complex analytical methods, difficult to understand technology, and difficult to interpret results. Interpretability technologies are technical means that enable users to understand and trust the decision-making results ofintelligence analysis. [Meth? od/process] The paper first introduces the development trend of intelligence analysis activities, and expounds the inter? pretation necessity of intelligence analysis process from four aspects:intelligence information acquisition, intelligence processing, intelligence analysis and decision-making services. Then, the interpretation types of intelligence analysis methodsare elaborated, including the combination of global interpretation and local interpretation, post-hoc interpreta? tion, swarm intelligence decision interpretation and people- oriented interactive interpretation. [Result/conclusion] The interpretation technologies of information analysis methods in the field of intelligence include causal inference, fea? ture importance analysis, rule interpretation, knowledge reasoning, and visual analysis. Commonly used intelligence analysis interpretation evaluation methods include qualitative analysis based on subjective judgment, semi-quantita? tive analysis based on heuristics, and quantitative analysis based on index evaluation. Finally, the main interpretable techniques of intelligence analysis in different fields and research issues that should be emphasized in the future devel? opment are summarized.