Marital Satisfaction Association Algorithm Based on Natural Language Processing and Deep Neural Network

Marital satisfaction is an important topic in psychology and sociology research, affecting individual mental health, family stability and social harmony. Traditional marital satisfaction research mainly relies on questionnaires and statistical modeling, but these methods are difficult to capture multi-dimensional, unstructured emotional interaction patterns. This study proposes a marital satisfaction prediction algorithm based on deep neural network (DNN), combined with natural language processing (NLP), sentiment analysis (Sentiment Analysis) and social network behavior modeling (Social Network Behavior Modeling), to build a multimodal data fusion framework to improve prediction accuracy. The study uses an interpretable artificial intelligence method to analyze the key factors affecting marital satisfaction and its dynamic changes, so as to provide intelligent decision-making support for marital relationship intervention and psychological counseling. Experimental results show that this method can achieve better prediction performance on multiple marital satisfaction datasets, and effectively reveals the influence mechanism of individual characteristics, emotional patterns and social environment variables on marital relationships.