# Research on coupling coordination of new quality productive forces and innovation resource allocation based on MLP neural networks

## TL;DR

This study uses an MLP neural network to analyze how new productive forces and innovation resources interact and evolve in China from 2012 to 2022.

## Contribution

A novel unsupervised dual-tower MLP model is proposed to evaluate coupling coordination between productive forces and resource allocation.

## Key findings

- The MLP model effectively captures long-term trends in coordination despite short-term fluctuations.
- The overall coordination degree is improving, but spatial disparities persist across China's regions.
- Spatial spillover effects emphasize the need for cross-regional cooperation and resource sharing.

## Abstract

The synergistic development of new quality productive forces (NQPF) and innovation resource allocation is critical for achieving sustainable and high-quality economic growth. Using provincial data from 2012 to 2022 in China, this study constructs the evaluation framework for NQPF and innovation resource allocation, and employs an unsupervised dual-tower multilayer perceptron (MLP) neural network model to measure the coupling coordination degree. And the spatial differentiation and dynamic evolution of the coordinated degree are further explored. The results demonstrate that the MLP approach offers superior performance in identifying long-term trends while remaining robust to short-term fluctuations. Despite remaining at a primary stage, the overall coordination degree exhibits a distinct upward trajectory. Spatial disparities are primarily driven by interregional differences, with the eastern region exhibiting short-term positive development cycles, the central region showing steady catch-up progress, and the western region facing challenges of marginalization. Moreover, significant spatial spillover effects highlight the influence of geographical proximity, underscoring the importance of cross-regional cooperation and innovation resource sharing.