With the increasing role of households in the macroeconomic system, understanding and predicting household financial decision-making behavior has become an important topic in financial research and policy making. This paper constructs a systematic analytical framework for modeling and simulating household financial behavior based on time series algorithms. By empirically analyzing the dynamic characteristics of household income, expenditure, savings, liabilities and other financial variables, it reveals the time-series and nonlinear patterns of household financial decisions. In this paper, time series modeling methods such as ARIMA and LSTM are used to model and forecast household financial behaviors and simulate the household decision response process under a variety of macroeconomic scenarios. The experimental results show that the deep time series model has significant advantages in capturing the complex dynamics of household financial behavior. Based on the model output, this paper further explores feasible options to enhance household financial stability and optimize the path of policy intervention. The findings can provide data support and decision-making basis for household financial risk early warning, financial product design and precise policy formulation.