Aiming to develop technology that facilitates behavioral change for energy conservation in home, our study examined behavioral vectorization and behavioral network analysis. These technologies are designed to discern the behavioral tendencies of individuals and groups, and to examine the connections between behaviors. We installed sensors in the living spaces of households, converting behavioral data into text. We then applied Word2Vec to this text data, learning vector representations of words that depict various behaviors in these living spaces. By clustering these words, we visualized individual behavioral tendencies. The result provided insights into customizing behavioral change support according to individual characteristics. Additionally, through the analysis of behavioral network created based on clustering, we identified the central behavior within the group and similarities in behavioral patterns among members. These findings suggest that understanding the behavioral tendencies can lead to effective interventions, thereby enhancing the impact of behavioral change support.