A nonlinear decoupling algorithm based on the white shark optimization algorithm optimized extreme learning machine （WSO-ELM） is proposed to address the issue of inter dimensional coupling interference in three-dimensional force sensors， with an integrated three-dimensional force sensor based on fiber Bragg grating （FBG） as the research object. Firstly， an integrated three-dimensional force sensor based on FBG was designed to reveal the mapping relationship between the wavelength drift of the sensor and the three-dimensional force； Then， a static calibration experimental system is established to analyze the three-dimensional force coupling characteristics， and a WSO-ELM algorithm three-dimensional force sensor decoupling model is established. The model is optimized using the stable and efficient characteristics of the white shark optimization algorithm （WSO） to find the optimal parameter combination of the number of neurons in the hidden layer of the ELM neural network and the decoupling time. Research on nonlinear decoupling of three-dimensional force sensors based on WSO-ELM is carried out； Finally， after decoupling， the maximum average type I error of the sensor reaches 0.51%， and the maximum average type II error reaches 0.65%， achieving three-dimensional force nonlinear decoupling based on WSO-ELM. To verify the decoupling effect， a comparative experiment was conducted between the WSO-ELM algorithm and the extreme learning machine neural network model， backpropagation neural network， and least squares method for decoupling effect. The experimental results show that the WSO-ELM algorithm has good decoupling effect， can effectively construct the coupling relationship between three-dimensional force dimensions， reduce sensor coupling interference， improve sensor measurement accuracy， and has good nonlinear decoupling ability.