【Objective】Forest tree breeding is shifting from experience-driven selection to data- and model-driven improvement, creating a need for an integrated framework that links phenotypic analysis, multi-omics interaction modeling, and breeding decision support. This paper focuses on phenotypic variable construction and complex-trait interaction analysis in smart forest tree breeding, reviews the technical pathways of phenotypic analysis and multi-omics interaction algorithms, and discusses their roles and future directions in intelligent breeding systems.【Method】We first discuss the general workflow of forest tree phenotypic analysis—covering multi-source sensing, data processing, intelligent analysis, and the construction of structural phenotypic variables—and elucidate its interrelationships with genetic analysis, genomic selection, and breeding decision-making. Subsequently, addressing the mechanisms by which genetic factors, environmental factors, molecular regulation, and developmental processes collectively shape complex forest tree phenotypes, we summarize key technical approaches such as multi-omics data integration, unified representation learning, genotype-environment interaction (G×E) modeling, and the prediction and mechanistic analysis of complex traits. Furthermore, we explore the roles that large multi-omics models, scientific research agents, and digital twins play in knowledge management, task scheduling, and the formulation of breeding plans.【Result】Based on the aforementioned findings, we conclude that the primary focus of smart forest tree breeding lies not merely in the predictive accuracy of isolated models, but rather in the comprehensive end-to-end process encompassing multi-source data acquisition, phenotypic variable extraction, interaction modeling, and ultimately interpretive feedback. Currently, smart forest tree breeding faces several challenges, including low levels of data standardization, issues related to small sample sizes coupled with high variability, difficulties in adapting to novel environments, a lack of robust interpretability, and an absence of effective closed-loop validation mechanisms.【Conclusion】In the future, it is necessary to accelerate the construction of a long-term, multi-site, and multi-scale collaborative phenotypic-genotypic-environmental data infrastructure, and to develop multimodal foundation models, causal inference methods, and human-machine collaborative agent systems suited to the long life cycle and strong environmental heterogeneity of forest trees. On this basis, a smart forest tree breeding system can be established that integrates data acquisition, trait analysis, interaction modeling, strategy generation, and breeding validation.