The quantitative analysis of electricity spot market clearing results is vital for independent system operators to monitor and mitigate market abnormalities that could potentially lead to the abuse of market power. The increasing complexity and expansion of spot markets, driven by growing market participation, have significantly amplified the challenge and importance of clearing result analysis. In this paper, a framework for decomposing the factors influencing the locational marginal price (LMP) is proposed, aiming to quantitatively assess the causes behind clearing results. A day-ahead security-constrained hydro-thermal economic dispatch model is established to minimize the total bidding price. Based on this model, the LMP at each node is deduced in detail using the optimization conditions, in which the LMP of non-marginal nodes can be decomposed into a linear combination of local sensitivity factors and the LMPs of marginal nodes, with each factor corresponding to a specific constraint affecting the LMP. To efficiently solve the proposed framework, an advanced matrix preconditioning method integrated with commercial optimization solver is developed to compute the local sensitivity factors, providing enhanced computational efficiency for large-scale power systems. Finally, the proposed framework is validated using the IEEE 30-node system and the significantly larger 2746-node Polish system.