As China enters the “15th Five-Year Plan” period, its socialist modernization drive has reached a critical stage of surmounting major obstacles and tackling tough challenges. The traditional extensive growth model, which relies solely on material capital accumulation, can no longer meet the demands of high-quality development. How to achieve the close integration between investment in goods and investment in people, thereby stimulating new quality productive forces and enhancing total factor productivity, has become a core proposition that urgently needs to be addressed. This paper constructs a multi-level indicator system. The investment-in-goods level primarily comprises three dimensions: digital-intelligent infrastructure investment, ecological and environmental protection investment, and real economy investment. The investment-in-people level mainly consists of four dimensions: education and talent cultivation investment, health protection investment, employment and job utilization investment, and social security support investment. The entropy weight method is employed for measurement, and the coupling coordination degree model is applied to evaluate the close integration level between investment in goods and investment in people. Using panel data from 284 prefecture-level cities in China from 2013 to 2023 as the sample, and incorporating models such as the Dagum Gini coefficient, kernel density estimation, spatiotemporal Markov chain, and convergence models, this study systematically examines the regional disparities and spatiotemporal evolution characteristics of the close integration level between investment in goods and investment in people. The results indicate that the close integration level between investment in goods and investment in people exhibits a sustained upward trend, with the steady increase in the investment-in-people level serving as the primary driving force, while the growth rate of the investment-in-goods level has slowed in the later period. Regional differences mainly originate from changes in the transvariation density of the Gini coefficient. Further decomposition using the Theil index reveals that inter-provincial differences constitute the main source of regional disparities in the close integration level. The close integration level demonstrates significant positive spatial correlation across regions, forming a core-periphery structure and exhibiting lag effects. Markov chain analysis shows that the state transition paths of the close integration level between investment in goods and investment in people exhibit strong path dependence. The probability of low-level regions transitioning upward to higher levels has gradually increased, while high-level regions display a pronounced locking effect. Convergence analysis reveals that although the close integration level between investment in goods and investment in people displays σ-convergence characteristics across regions, it has not achieved fully synchronized convergence. Spatial β-convergence analysis indicates the existence of both absolute and conditional β-convergence. The eastern region exhibits the fastest convergence speed, while the central region is the slowest. After incorporating control variables, the overall convergence speed slightly decreases. Based on these findings, the paper proposes policy recommendations such as strengthening regional coordination, promoting the synergistic "people-goods" development mechanism, facilitating cross-regional collaboration, and optimizing convergence pathways, so as to further elevate the close integration level between investment in goods and investment in people.